12 Lessons to Get Started Building AI Agents


Autogen + GPTs - build a swarm AI researchers


Web UI for AutoGen (A Framework Multi-Agent LLM Applications)


Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI


Microsoft's GraphRAG + AutoGen + Ollama + Chainlit = Fully Local & Free Multi-Agent RAG Superbot


Drag & drop UI to build and run a flow of autogen AI agents


Neuralink for your AI Agents - LangChain - Autogen - CrewAI


Building LLM-Enabled Multi Agent Applications from Scratch


This repository holds enhanced Agents, built for the Microsoft AutoGen Framework. Debuting with a MemoryEnabledAgent with improvements in context/token control, portability, and PnP functionality.


A multi-agent event planning workflow built with Microsoft Agent Framework - combining Semantic Kernel's enterprise orchestration with AutoGen's multi-agent patterns.


Agent Cloud is like having your own GPT builder with a bunch extra goodies. The GUI features 1) RAG pipeline which can natively embed 260+ datasources 2) Create Conversational apps (like GPTs) 3) Create Multi Agent process automation apps (


Learn to build and customize multi-agent systems using the AutoGen. The course teaches you to implement complex AI applications through agent collaboration and advanced design patterns.


MagenticLite is an experimental agent that works across the browser and local file system


A CLI tool for threat modeling and visualizing AI agents built using popular frameworks like LangGraph, AutoGen, CrewAI, and more.


Experimenting with AutoGen to see if an entire book can be written with AI agents


Agent with vision ability via llava & autogen


AutoGen AGI: Advancing AI agents using AutoGen towards AGI capabilities. Explore advanced enhancements in group chat dynamics, decision-making, and complex task proficiency. Join our journey in shaping AI's future!


This repo helps you to build a team of AI agents with Autogen


The fastest way to bring multi-agent workflows to production.


Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together smoothly, tackling complex tasks.


A collection of examples that show how to use CrewAI framework to automate workflows.


Extend the capabilities of your CrewAI agents with Tools


A user-friendly, multi-platform GUI for managing and running CrewAI agents and tasks. Supports Conda and virtual environments, no coding needed.


Experiments with local as well as models available through an api


A curated list of open-source projects built by the CrewAI community. Discover, contribute, and extend the possibilities of AI agents with CrewAI.


Your CrewAI Powered Video Editing Assistant


Interactive user interface for CrewAI package.


使用CrewAI+FastAPI搭建多Agent协作应用并对外提供API服务，同时支持gpt、国产大模型、Ollama本地大模型。


基于AKShare和CrewAI的A股智能分析平台，通过多Agent协作提供专业的A股投资分析。  🚀 项目特色 📊 全面的A股数据分析：实时行情、财务数据、资金流向、市场情绪 🤖 多Agent协作：4个专业化AI角色协同工作 🇨🇳 A股市场特色：针对中国股市特点优化分析 📈 专业分析工具：基于AKShare的专业数据源 🎯 智能投资建议：综合分析提供投资决策参考


A collection of notebooks, cookbooks, and recipes showcasing fun and effective ways to use CrewAI's agentic workflow implementations and tools.


Multi-agent system that automates Gmail inbox management: categorizes emails, applies labels, and generates contextual responses using CrewAI


WIP - Allows you to create DSPy pipelines using ComfyUI


Collection of Claude Skills for DSPy framework - program language models, optimize prompts, and build RAG pipelines systematically


DSPy program/pipeline inspector widget for Jupyter/VSCode Notebooks.


Experiments with DSPy — declarative, trainable LLM pipelines. Notebooks and demos for evaluating, composing, and optimizing LLM workflows (code-gen, company valuation, market analysis).


Strwythura: construct an entity-resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology pipeline, plus context engineering for optimizing AI application outcomes within a specific dom


2-week research spike: DSPy programmatic prompting + MLflow lifecycle tracking for Text-to-ESQL pipeline


Declarative prompting and optimization for LangChain-style pipelines. Enterprise ready.


Designing a RAG pipeline using Gemma-2b, DSPy, and Qdrant


Advanced RAG pipeline optimization framework using DSPy. Implements modular RAG pipelines with Query-Rewriting, Sub-Query Decomposition, and Hybrid Search via Weaviate. Automates prompt tuning and few-shot selection using GEPA, SIMBA, MIPRO


implementations of DSPy programmed pipelines


AutoGEPA automates the tedious parts of setting up a DSPy optimization pipeline: converting raw data into dspy.Examples, generating a metric file with an LLM, splitting datasets, running baselines, and training with GEPA.


From your first dspy.Predict to enterprise deployment. A comprehensive guide to building programmatic language model pipelines with DSPy,  RLMs, and Reinforcement Learning. Why it works: It hits the high-value technical keywords (RLM, Reinf


A complete set of requirements—covering UX, CLI, and code—that builds on the previous pipeline for training and optimizing ONNX models with test‑time compute methods using DSPy. This document specifies user stories, command‐line interface a


RL-driven framework that composes modular DSPy pipelines and teleprompters to improve LLM reasoning (experiments use GPT-2).


A Claude Code skill for building DSPy extraction, classification, and analysis pipelines


Practical DSPy RLM demo for extracting structured fields from long documents using Python REPL reasoning, Azure OpenAI, and Markdown-based PDF pipelines.


DSPy pipeline that optimizes a news-summarization prompt using newsdata.io as the data source.


Shows the RAG and DSPY Pipeline with chunking


LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agent


LangChain for Go, the easiest way to write LLM-based programs in Go


Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain


😎 Awesome list of tools and projects with the awesome LangChain framework


Elixir implementation of a LangChain style framework that lets Elixir projects integrate with and leverage LLMs.


A curated list of awesome LLM agents frameworks.


Build LLM-powered Dart/Flutter applications.


Large Language Models (LLMs) tutorials & sample scripts, ft. langchain, openai, llamaindex, gpt, chromadb & pinecone


Semantic cache for LLMs. Fully integrated with LangChain and llama_index.


Practical LangChain patterns and implementations for real-world LLM applications. 极客时间：LangChain实战课 - 这是LangChain框架早期设计的一系列重点模块的直接而清晰的示例和讲解。随着LangChain的快速演进，有些代码需要安装新的版本进行迭代。希望大家在快速浏览课程概念（仍有价值）的同时，自行学习LangChain最新的代码和进展。


Everything you need to know to build your own RAG application


Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete GPU such as Arc, Flex and Max); seamles


A collection of apps powered by the LangChain LLM framework.


Get started with building Fullstack Agents using Gemini 2.5 and LangGraph


🚀 LangGraph for Java. A library for develop AI Agentic Architectures in the Java ecosystem. Designed to work smoothly with both Langchain4j and Spring AI.


Framework to build resilient language agents as graphs.


AI PDF chatbot agent built with LangChain & LangGraph


Hands-on LangGraph course repo for building production-grade LLM agents with Agentic RAG, ReAct, and reflection workflows.


An index of the LangChain + LangGraph ecosystem: concepts, projects, tools, templates, and guides for LLM & multi-agent apps.


A production-ready FastAPI template for building AI agent applications with LangGraph integration. This template provides a robust foundation for building scalable, secure, and maintainable AI agent services.


LangGraph-powered ReAct agent with Model Context Protocol (MCP) integration. A Streamlit web interface for dynamically configuring, deploying, and interacting with AI agents capable of accessing various data sources and APIs through MCP too


这是一个 LangChain 1.0 和 LangGraph 1.0 的学习仓库，学习如何进行agent开发，涵盖从基础概念到实战项目的完整学习路径。


LangGraph template for a simple ReAct agent


LangChain, LangGraph Open Tutorial for everyone!


Learn about the fundamentals of LangGraph through a series of notebooks


🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming


A tutorial based on MetaGPT to quickly help you understand the concept of agent and muti-agent and get started with coding development. 基于MetaGPT的多智能体入门与开发教程


开源多Agent智能体协作框架MetaGPT核心概念介绍及相关案例测试


这个仓库主要用于记录以MetaGPT为基础的Agent开发理论学习记录


[FORGE 2025] Incorporating Agile methodology into agents to create complex real-world softwares


A game of pong made by MetaGPT and ChatGPT Code Interpreter


Aitino is a platform that allows for the creation of crews of AI Agents to help you automate tasks and solve complex problems.


A streamlit application to play with LLM Models


⭐ Curated list of AI Agent frameworks: AutoGen, CrewAI, MetaGPT, LangGraph, Swarms. Multi-Agent Systems, Swarm Intelligence, A2A Protocol, MCP Protocol. LLM Agents, Agentic AI, Agent Workflows.


Some examples come from MetaGPT framework.


Dashboard metadata chatbot for executive officers to retrieve information and guidance to desired information, for corporation with many dashboards, using data dictionaries to connect source information among all dashboards.


A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.


A powerful Go framework for building production-ready AI agents!


Build your own Cowork, AI Scientist and other SoTA Agents just by editing config files. Support anthropic skills. An infinite-horizon agent framework designed for long-running, complex tasks.


A database of SDKs, frameworks, libraries, and tools for creating, monitoring, debugging and deploying autonomous AI agents


Python SDK for transforming any AI agent into a production-ready application. Framework-agnostic primitives for runtime, memory, authentication, and tools with AWS-managed infrastructure.


Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph


The official Python SDK for the Agentica agent framework from Symbolica


ERNIE Bot Agent is a Large Language Model (LLM) Agent Framework, powered by the advanced capabilities of ERNIE Bot and the platform resources of Baidu AI Studio.


A powerful TypeScript framework for building non-deterministic AI agents with advanced cognitive capabilities like reasoning, decision-making, and inter-agent collaboration within the OpenServ platform. Built with strong typing, extensible


Angular SDK for Building Agentic Apps + Generative UI


Naptha is a framework and infrastructure for developing and running multi-agent systems at scale with heterogeneous models, architectures and data


The official TypeScript SDK for the Agentica agent framework from Symbolica


A flexible toolkit for building, running, and managing AI-powered agents - built on top of the Vercel AI SDK. Integrates smoothly with your favorite frameworks.


the framework/ sdk that lets you build browser controlling agents in 3 lines of code. join chat @ https://discord.gg/umgnyQU2K8


Portable semantic memory for AI agents: core engine, TypeScript SDK, framework adapters, MCP server, CLI, and host plugins.


Open Source A2A Protocol Adapter SDK for Different Agent Frameworks


一个为ElectronBot机器人和瀚文键盘（HelloWorldKeyboard）开发的上位机软件（包含机器人USB操作SDK和瀚文键盘HID操作SDK），采用Windows App SDK框架编写界面。   A host computer software developed for ElectronBot robots (including robot USB operation SDK), using the Windows App SDK framework to


A Deep Agent Harness framework built with Vercel's AI SDK v6


Three working examples showing how AI agent development evolved - from traditional RAG to batteries-included SDKs and skill-based frameworks


This repository contains the Hugging Face Agents Course.


An example repository to use HuggingFace smolagents, Phidata and CrewAI frameworks with local LLMs


A lightweight code assistant with tool-using capabilities built on HuggingFace's smolagents.


Final project for the Hugging Face AI Agents course testing smolagents on the GAIA benchmark


A framework to orchestrate multiple AI agents, enabling them to collaborate efficiently through smart threading, dependency management, context sharing, and distributed execution via a built-in swarm system. Built on top of smolagents, it a


Drone autonomy powered by Generative AI (HuggingFace smolagents + Google Gemini)


Code Agents with the HuggingFace Smolagents AI Agent Framework


Marimo versions of HuggingFace's Agents course notebooks.


A multi-agent system leveraging Hugging Face's smolagents to automate content creation by coordinating research, writing, and editing tasks through collaborative AI agents.


Unofficial TypeScript/Node.js port of HuggingFace's smolagents — run LLM-powered agents with tool calling, streaming, and code execution in just a few lines.


smolagents tools for The Colony (thecolony.cc) — give any HuggingFace agent the ability to search, read, write, and interact on the AI agent internet


Self-correcting AI agent for natural language to SQL using HuggingFace smolagents and ReAct framework


SmolAgents is a Python-based framework that allows you to create AI agents capable of handling various tasks, such as basic Q&A, web searches, image generation, and downloading the most popular models from the Hugging Face Hub. This reposit


An HF's Smolagent to automate molecular dynamics simulations using GROMACS.


Counter-UAS interceptor drone engagement simulation — full kill chain from detection through intercept


Multi-sensor fusion system for counter-UAS drone detection


This repository contains the single-page application (SPA) frontend for the Counter-UAS (C-UAS) system. It serves as the users primary interface for real-time situational awareness.


A full-stack project demonstrating AI-powered drone detection inspired by counter-UAS defense systems.


Computer Vision based counter-uas threat detection system, which utilizes a custom cnn and public datasets.


This repository contains the backend orchestration layer for the Counter-UAS (C-UAS) Human-Machine Interface (HMI) system, focused on providing real-time threat detection awareness to individual users at the edge.


Counter-UAS telemetry/log analyzer that flags drone-detection events, RF bands, and track anomalies.


Simulation framework for real-time volumetric 3D detection, tracking, and engagement of multi-drone swarms, with advanced sensor array modeling, detection range analysis, and publication-ready visualizations for counter-UAS research.


RF-Based Drone Detection, Payload Analysis & Threat Classification System - Affordable open-source Counter-UAS research


This project implements a high-performance RF Signal Detection pipeline on an FPGA specifically designed for Portabel/Handheld Counter-UAS (Unmanned Aircraft Systems) Hardware. The design focuses on real-time drone signal identification whi


meshkah is an academic Counter-UAS (CUAS) prototype that fuses RGB, thermal, RF, and acoustic detections to estimate and track drone position in 3D. It integrates ROS 2 Jazzy, Gazebo Harmonic, and ArduPilot SITL, supports automated flight s


Real-time drone detection and tracking using YOLOv11x with heatmap visualization. Trained on custom UAV dataset, optimized for small, fast-moving targets.


Aerial Object Detection using a Drone with PX4 Autopilot and ROS 2. PX4 SITL and Gazebo Garden used for Simulation. YOLOv8 used for Object Detection.


🔥 CVPR 2025 (Nashville TN) - The 4th Anti-UAV Workshop & Challenge 🥉


(Outdated) A UAV object detection system based on YOLOv3, providing an interactive interface and `VisDrone2018` training scheme.


Real-time YOLOv8n UAV detection on the RK3588S NPU | saturates the sensor at 46 FPS | ~140 MB RAM | fully hardware-accelerated (ISP+RGA+NPU) | on-device LLM scene summaries


复现南京理工大学一篇论文：A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11


:jack_o_lantern:Implementation of YOLOv3 about uav detection in PyTorch


Sim2air: synthetic texture-invariant dataset for object detection of UAVs


Real-time military drone detection system using YOLO11. Detects Shahed-136, Lancet, Orlan-10, and other UAVs with 94.8% mAP50 accuracy. Features Streamlit interface with overlay statistics, video processing, and webcam monitoring. Trained o


This project implements a real-time image and video UAVs(unmanned aerial vehicle) detection classifier using a new trained yolov3 model.


This repository presents an innovative approach to automate tree counting using YOLOv5, a high-performing object detection model, coupled with high-resolution UAV imagery. By leveraging the power of deep learning, our algorithm intelligent


This repository was developed in the context of the Pervasive AI Developer Contest with AMD


SOD-YOLO (Small Object Detection YOLO) builds upon the foundational YOLOv8 model to address the unique challenges of detecting small objects in complex backgrounds typical of UAV imagery. This repository contains the code for the model, inc


YoloOW: A Spatial Scale Adaptive Real-time Object Detection Neural Network for Open Water Search and Rescue from UAV Aerial Imagery


Object Tracking of UAVs utilizing YOLOv8 model for detection and classification and utilizing the Multiple Instance Learning (MIL) tracker for object tracking.


A Robust and Efficient Multi-Scenario Object Detection Network on Edge Device for UAV


Yolov11 uav detection using anti-uav dataset


UAV Object Detection using transfer learning with YOLOv5x


Enhanced YOLOv8 for Small-Object Detection in Multiscale UAV Imagery: Innovations in Detection Accuracy and Efficiency


SENTINEL-X adalah proof-of-concept platform yang dikembangkan sebagai simulasi dan demonstrasi konsep untuk sistem threat intelligence dan monitoring multi-domain. Proyek ini berawal dari ide untuk membuat platform yang bisa mengintegrasika


End-to-end AI solution for Maritime Domain Awareness featuring vessel detection from EO/SAR imagery, length-width-heading estimation, AIS correlation, vessel classification, and path interpolation/prediction. Fully dockerized pipeline with


AIS January data for port classification and ETA estimation using a multi-task LSTM model with spatio-temporal sequential features, preprocessing, and trajectory segmentation.


Detecting illegal maritime activity by cross-referencing real-time satellite detections with AIS broadcasts. Built to combat IUU (Illegal, Unreported, and Unregulated) fishing and enhance maritime domain awareness.


Maritime domain awareness for the Southeast Coast + Caribbean, built from 100% public data. AIS gaps, loitering, and rendezvous detection. Built by OCEANS LLC, Jacksonville.


This real-time anomaly detection system exposes deceptive shipping practices utilized by the 'shadow fleet' for illicit operations. By processing live AIS telemetry from the Strait of Malacca, it mathematically flags GPS spoofing and 'Dark


Real-time Maritime Domain Awareness system that processes AIS data, SAR imagery, and vessel tracking streams to detect illegal fishing, smuggling, and dark vessel activity. It leverages big data pipelines and machine learning to model norma


Object (e.g Pedestrian, vehicles) tracking by Extended Kalman Filter (EKF), with  fused data from both lidar and radar sensors.


A curated list of radar datasets, detection, tracking and fusion


Mac app to simplify posting bug reports to Apple's Radar bug tracking system.


A tool for BLE environment monitoring. Find and track Bluetooth devices around, and get notified when the target device is detected.


object tracking based on millimeter wave radar


React Native module for Radar, the widely-used geofencing and location tracking platform


Allows users to pull in new song releases from their favorite artists and provides users with important metrics like their top tracks, top artists, and recently played tracks, queryable by time range.


Object (e.g Pedestrian, biker, vehicles) tracking by Unscented Kalman Filter (UKF), with  fused data from both lidar and radar sensors.


iOS SDK for Radar, the widely-used geofencing and location tracking platform


Android SDK for Radar, the widely-used geofencing and location tracking platform


ECE 2595 Radar Signal Processing Projects


Code used to build a Tracker Radar data set from raw crawl data.


Generation scripts and source for Tracker Radar Wiki


🕵Track prices of the Hetzner dedicated server auction


[ICRA2024] RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud


Web JavaScript SDK for Radar, the widely-used geofencing and location tracking platform


This code focusses on Object Tracking and Speed Estimation of vehicles passing by a road.


🌎📡 TypeScript Astrodynamics Software for Non-Engineers. 3D Visualization of satellite data and the sensors that track them.


論文學習雷達 — a personal literature-tracking & learning radar: dozens of journal/PubMed feeds → interest-scored → private Cloudflare-Access web triage → flows into your notes


Flutter package for Radar, the widely-used geofencing and location tracking platform


Sensor fusion calculating yaw, pitch and roll from the outputs of motion tracking devices


LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking


Official code for "EagerMOT: 3D Multi-Object Tracking via Sensor Fusion" [ICRA 2021]


Multi-Modal Sensor Fusion and Object Tracking for Autonomous Racing


Cross-platform library for 6DoF tracking of the PS Move Motion Controller. Sensor fusion, computer vision, ambient display (LED orb).


Learn to detect obstacles in lidar point clouds through clustering and segmentation, apply thresholds and filters to radar data in order to accurately track objects, and augment your perception by projecting camera images into three dimensi


Sensor Fusion, Project 3: 3D Object Tracking


This project performs sensor fusion to track a mobile device's orientation. The data utilised are from three sensors: a) Accelerometer, b) Magnetic Field, d) Gyroscope. The sensor fusion is executed off-line. This project basically ports co


Implemented Unscented Kalman Filter (UKF) for orientation tracking. Sensors fusion of accelerometer, and gyroscope


Combining sensor inputs from camera/lidar/radar to detect, track moving vehicles and estimate their speed and direction.


An open-source project for accurate indoor pedestrian localization using WiFi, PDR, and EKF fusion. It combines WiFi signals, step tracking, and sensor fusion for reliable positioning in GPS-denied environments.


Collision Avoidance System with Camera and Lidar - Track an Object in 3D Space - Object Detection with YOLO - Descriptors and Keypoint Matching


Benchmark dataset for maritime multi-sensor, multi-target tracking


This ROS2 package integrates data from a 360-degree lidar and a camera to achieve enhanced object tracking through sensor fusion.


Radar and Lidar Sensor Fusion using Simple, Extended, and Unscented Kalman Filter for Object Tracking and State Prediction.


A Kalman Filter-Guided Sensor Fusion For Robust Object Tracking in Dynamic Environments using RGB-Depth-LiDAR sensors


GPU based real-time camera tracking, volumetric fusion and volume rendering system. Addressed updating the map with moving sensor and the static sensors.


Udacity, Sensor Fusion, Project of SFND_3D_Object_Tracking


A carbon-aware ML training scheduling system that optimises machine learning workloads using real-time UK grid carbon intensity data. It simulates ML training, finds optimal low-carbon execution windows, and visualises carbon savings throug


Learn to optimize machine learning tasks for environmental sustainability. Discover how to use real-time electricity data and low-carbon energy sources for model training and inference, reducing the carbon footprint of your cloud operations


Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service


AI Sustainability Assistant is a smart web platform that analyzes lifestyle data to calculate carbon footprint and suggests personalized eco-friendly actions. The system combines web development, data analysis, and machine learning to encou


Carbon Aware Time Shifiting. Machine learning API to encounter carbon intensity into execution of energy intensive processes.


Joint Optimization of Workload Scheduling and Cooling


A web-based AI application that analyzes individual lifestyle data to calculate, predict, and reduce personal carbon emissions through visualization, machine learning, and personalized recommendations.


Carbon-Aware Machine Learning Framework with Automated Emission Enforcement


A Carbon-Aware DevOps framework designed to optimize energy efficiency in AI-driven crop yield prediction. This project implements GreenOps principles to minimize the environmental footprint of machine learning pipelines while maintaining h


AI-CARE: Carbon-Aware Reporting and Evaluation for Machine Learning Models


During October 2025 until January 2026, the project in general runs through three phases of experiments and shows an investigation of the effectiveness of carbon aware scheduling. The items uploaded here are two of the phases that I did on


AI-powered Carbon Footprint Awareness Platform built with React, Flask, PostgreSQL and Machine Learning.


Carbon-aware machine learning benchmarking framework evaluating predictive performance alongside energy consumption and CO₂ emissions.


A machine learning–based project that analyzes carbon emission data and predicts future emissions to promote sustainability, green skills, and data-driven environmental awareness.


Feasibility-aware analysis of latent representations learned by multitask machine-learning models for electrochemical energy materials. The framework reveals domain structure, transferability limits, and data-supported performance regimes a


Developed a machine learning model to predict household carbon emissions based on lifestyle and  consumption patterns. The project aimed to raise awareness of environmental impact at the household  level and provide personalized recommendat


Developed a machine learning model to predict household carbon emissions based on lifestyle and consumption patterns. The project aimed to raise awareness of environmental impact at the household level and provide personalized recommendatio


Carbon Cost Calculator is a machine learning-powered web application that estimates the carbon emissions and environmental cost of a vehicle based on its fuel type, engine size, and mileage. Built using Python, Streamlit, and MySQL, it help


Learn how to measure a machine learning model's carbon footprint and practice strategies that can help shrink the energy involved in training these models.


Multimodal meme hate detection using VLMs and Fusion Approaches, with training evaluation and carbon emission tracking via CodeCarbon.


CarbonLens - Track Carbon Emissions of Training AI Models


CarbonIQ 🌍 is a Python desktop app that estimates the energy consumption and CO₂ emissions of ML model training. Compare multiple models, view animated charts, and export detailed PDF reports to track your AI projects’ environmental impact—


GreenModel: AI Carbon Emission Tracker is a user-friendly Streamlit platform that helps users train machine learning models while tracking their carbon emissions in real time. It promotes sustainable AI development by using the CodeCarbon l


Eco-Ops is a carbon-aware ML framework that tracks energy usage and CO₂ emissions during model training. It uses CodeCarbon and Streamlit to provide benchmarking, visualization, and sustainability analysis for machine learning models.


[IEEE WCNC] Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surfaces


[JSAC 2018] Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach


[TMC 2023] Delay-Sensitive Energy-Efficient UAV Crowdsensing by Deep Reinforcement Learning


The official repository for the paper with the same name.


This repository hosts a simple demonstration of a deep learning approach for the inverse design of patch antennas. The goal is to explore energy-efficient designs and to significantly reduce simulation cost compared to conventional methods.


[KDD 2021] Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning


[TMC 2021] Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning


This project develops a deep reinforcement learning agent to recommend energy-efficient building designs by optimizing parameters like window size, insulation thickness, and HVAC setpoints, aiming to minimize energy consumption and enhance


Actor-Critic Deep Reinforcement Learning for Energy-Efficient and Delay-Aware Task Offloading in UAV-Assisted Mobile Edge Computing.


[JSAC 2019] Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach


Deep Learning for Energy Efficient Beamforming in MU-MISO Networks: A GAT-based Approach


Harness the power of deep reinforcement learning to optimize your Home Energy Management System (HEMS). Our tailored agent, trained on the CoSES ProHMo Modelica framework, efficiently controls a building's heat pump and thermal storage valv


Deep Reinforcement Learning for Energy-Efficient Computation Offloading in Mobile-Edge Computing


Data and code for the paper "Experimentally realized \textit{in situ} backpropagation for deep learning in energy-efficient nanophotonic neural networks"


[INFOCOM 2020] Energy-Efficient UAV Crowdsensing with Multiple Charging Stations by Deep Learning


[ICDE 2020] Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach


The Decentralized Federated Deep Reinforcement Learning Framework for Energy Efficient Base Station Sleep Control


A reproduction of the paper "FPGA-based Deep-Learning Accelerators for Energy Efficient Motor Imagery EEG classification".


The energy sector is one of the largest and most important sectors out there. The ability to efficiently forecast hourly energy consumption plays an important role on how energy is distributed and consumed. Deep learning algorithms have pla


This project implements a memristor-based deep Spiking Neural Network (SNN) in Python, using Leaky Integrate-and-Fire neurons with adaptive thresholds and STDP learning for energy-efficient neuromorphic computing. It trains and tests on the


AI4Green is an Electronic Laboratory Notebook which combines data storage and sharing while promoting green and sustainable chemistry.


GreenRoots: An AI/ML project dedicated to promoting mangrove sustainability and fostering community engagement in mangrove conservation efforts.


AI-powered sustainable e-commerce platform with EarthScore ratings, group buying via DBSCAN clustering, and multi-agent chatbot. Gamifies eco-friendly shopping with real-time carbon tracking, achievements, and community bulk purchasing. Bui


GreenForce is an AI-powered sustainability assistant that helps organizations automate and coordinate environmental workflows aligned with the UN Sustainable Development Goals (SDGs 7, 12 and 13).


🌱 Green- AI-Powered Waste-to-Energy Platform Transform waste into sustainable energy using advanced AI technology. GreenTech combines real-time monitoring, predictive analytics, and community engagement to optimize waste processing and


This repository is a companion page for the revision at the 9th International Workshop on Green and Sustainable Software (GREENS’25)


An AI & Blockchain sustainability platform. Track eco-actions, get ML-verified rewards (Green Tokens), and support real-world NGOs. [React, Node.js, Python, Solidity, Document AI]


Blockchain-Driven Carbon Management integrates blockchain for transparency, automated credit systems, XRC-20 tokens for carbon trading, AI predective analysis, pooling tokens, reward system, user-friendly interfaces, incentives, compliance,


A green software AI skill + agent for AI projects to have a Green Software Sustainability expert in your app.


The Green Finance Optimization Platform uses AI to optimize investments in sustainable projects, focusing on ESG impact and providing decision-making insights.


This project has been created to participate "Cisco AI & Sustainability Code Challenge" contest.


Our daily choices significantly impact the environment, but it's often hard to track and maintain sustainable habits. This challenge is about using AI to empower individuals with tools that promote eco-friendly living. Choose a project belo


Student project from UTC providing solutions to assess carbon emissions and reduce costs in modern AI projects.


Blockchain-Driven Carbon Management integrates blockchain for transparency, automated credit systems, XRC-20 tokens for carbon trading, AI predictions, user-friendly interfaces, incentives, compliance, and public awareness. Revolutionizing


A curated collection of research, tools, and best practices for environmentally sustainable AI development and deployment.


A comprehensive, AI-driven green energy solution integrating renewable energy sources, IoT, data analytics, and sustainable infrastructure management.


Find out about sustainability in your favorite sport clothing brands, in just one click!


greensync is a decentralized ai platform that distributes computation across user devices, reducing reliance on energy-hungry cloud servers. users earn green coins based on their computational contributions, incentivizing sustainable ai pro


GPU and CPU measurements for ML inference workloads for power, latency and throughput


Machine Learning GPU Power Measurement on Chameleon Cloud


[ICLR 2025] Pyramidal Flow Matching for Efficient Video Generative Modeling


Generative Motion Latent Flow Matching for Audio-driven Talking Portrait


A geometric flow matching model for generative protein-ligand docking and affinity prediction. (ISMB 2025)


[ICCV 2025] Official Pytorch Implementation of FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait.


[NeurIPS 2025] Official repository for "Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling"


Bare-bones implementations of some generative models in Jax: diffusion, normalizing flows, consistency models, flow matching, (beta)-VAEs, etc


Pytorch implementation of "DeepFlow: History Matching in the Space of Deep Generative Models"


Awesome papers related to generative flow matching and its applications in bioinformatics. (Nature Machine Intelligence)


Real-Time Streamable Generative Speech Restoration with Flow Matching


All-atom generative protein design using SE(3) flow matching


Unifying flow matching and diffusion on Riemannian manifolds and discrete spaces, for generative deep learning.


Simple reimplementation of Flow Matching for Generative Modeling (https://arxiv.org/abs/2210.02747) paper in PyTorch


Minimal JAX implementation unifying Diffusion and Flow Matching algorithms as alternative strategies for transporting data distributions.


Generative backmapping of coarse-grained molecular systems using an equivariant graph neural network and flow-matching


Follow the Mean: controlling flow-matching generative models by shifting endpoint means with reference examples


P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching


A comprehensive JAX/NNX library for diffusion and flow matching generative algorithms, featuring DiT (Diffusion Transformer) and its variants as the primary backbone with support for ImageNet training and various sampling strategies.


A minimal PyTorch implementation of Flow Matching for Generative Modeling.


Official repo for "Generative Point Tracking with Flow Matching".


FourierFlow: Frequency-aware Flow Matching for Generative Turbulence Modeling


Official Implementation of Rectified Flow (ICLR2023 Spotlight)


Text-to-Music Generation with Rectified Flow Transformers


TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models


:zap: InstaFlow! One-Step Stable Diffusion with Rectified Flow (ICLR 2024)


PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator (NeurIPS 2024)


Implementation of rectified flow and some of its followup research / improvements in Pytorch


Minimal implementation of scalable rectified flow transformers, based on SD3's approach


Rectified Flow Inversion (RF-Inversion) - ICLR 2025


[ICLR 2025] Official implementation of Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration


[NeurIPS 2025, Spotlight] Rectified Point Flow: Generic Point Cloud Pose Estimation


[ICASSP 2024] This is the official code for "VoiceFlow: Efficient Text-to-Speech with Rectified Flow Matching"


[ICML2025] An 8-step inversion and 8-step editing process works effectively with the FLUX-dev model. (3x speedup with results that are comparable or even superior to baseline methods)


The official implementation of OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows


[CVPR 2024 oral]This repository contains the official implementation of "FlowIE: Efficient Image Enhancement via Rectified Flow"


[🚀ICML 2025] "Taming Rectified Flow for Inversion and Editing"  Using FLUX and HunyuanVideo for image and video editing!


[NeurIPS 2024] RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance


FlowTS: Time Series Generation via Rectiﬁed Flow


Implementation of RIFT-SVC, a singing voice conversion model based on Rectified Flow Transformer.


A summary of related works about flow matching, stochastic interpolants


Code for the paper: Probabilistic Forecasting with Stochastic Interpolants and Follmer Processes (generative AI for forecasting)


Definitive implementation of the stochastic interpolant framework for generative modeling in jax.


Stochastic interpolants are applied to super-resolve a case study of two-dimensional turbulence


[ICLR 2026] Official code for BézierFlow: Learning Bézier Stochastic Interpolant Schedulers for Few-Step Generation


Official Code for "Multitask Learning With Stochastic Interpolants", Spotlight NeurIPS2025


The official implementation of DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants


[ICCV 2025] Stochastic Interpolants for Revealing Stylistic Flows across the History of Art


Adversarial Distillation of Generative Models: A Unified View via Stochastic Interpolants


a Python library that implements the stochastic interpolant models and algorithms described in the paper "Stochastic Interpolants via Diffusive and Flow-based Processes" by Chen et al.


Using stochastic interpolants as a framework for modeling return distributions


Notes on all things Stochastic Interpolants & some implementation here and there.


Implementation of stochastic interpolants


MSF - Modular framework for multi sensor fusion based on an Extended Kalman Filter (EKF)


An unscented Kalman Filter implementation for fusing lidar and radar sensor measurements.


A simple Matlab example of sensor fusion using a Kalman filter


A Sensor Fusion Algorithm that can predict a State Estimate and Update if it is uncertain


Mad Location Manager is a library for GPS and Accelerometer data "fusion" with Kalman filter


Fusing Lidar and Radar data with Extended Kalman Filter (EKF)


Simulation Environment for Advanced Kalman Filtering for Sensor Fusion Course


Matlab code used for the paper " Invariant Kalman Filtering for Visual Inertial SLAM"


An extended Kalman Filter implementation in C++ for fusing lidar and radar sensor measurements.


Some basic algorithm used in Radar data process, including pulse compression/CFAR/monopulse、Kalman filter and fusion、arrary antenna design.


fusion location with only ranging and 6-axis imu sensor in similink data


MATLAB implementation of localization using sensor fusion of GPS/INS through an error-state Kalman filter.


State Estimation and Localization of an autonomous vehicle based on IMU (high rate), GNSS (GPS) and Lidar data with sensor fusion techniques using the Extended Kalman Filter (EKF).


Unscented_Kalman_Filter, CTRV model, Sensor fusion of lidar and radar, multiobject detection


An extended Kalman Filter implementation in Python for fusing lidar and radar sensor measurements


6-axis(3-axis acceleration sensor+3-axis gyro sensor) IMU fusion with Extended Kalman Filter.


ROS Error-State Kalman Filter based on PX4/ecl. Performs GPS/Magnetometer/Vision Pose/Optical Flow/RangeFinder fusion with IMU


Using an Extended Kalman Filter to calculate a UAV's pose from IMU and GPS data.


The code implemented in ROS projects a point cloud obtained by a Velodyne VLP16 3D-Lidar sensor on an image from an RGB camera.


[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation


ROS2 Package to calculate the intrinsic and extrinsic camera calibration. Adding an easy way to fuse camera and lidars


fully applied in ROS. simply fuse the category and location information


Offical PyTorch implementation of "BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework"


CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection


camera object tracking，lidar object tracking, camera and lidar fusion object tracking


[ICRA 2024] SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation


[RAL & IROS 2022] Lidar With Velocity: Correcting Moving Objects Point Cloud Distortion From Oscillating Scanning Lidars by Fusion With Camera


KiTTI LiDAR-Camera Fusion, implemented using kitti_ros.


Realtime Camera-Radar-Lidar Fusion detection in ROS simulation.


This repository uses a ROS node to subscribe to camera (hikvision) and lidar (livox) data. After the node merges the data, it publishes the colored point cloud and displays it in rviz.


LiDAR-Camera Fusion for 3D Object Detection in Autonomous Driving Systems


[ICRA 2025] Gaussian-LIC: Real-Time Photo-Realistic SLAM with Gaussian Splatting and LiDAR-Inertial-Camera Fusion


Code for RA-L journal and IROS 2022 paper "DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on  Camera-LiDAR Fusion with Deep Association".


An optimization-based multi-sensor state estimator


Multi-Sensor Fusion (GNSS, IMU, Camera) 多源多传感器融合定位 GPS/INS组合导航  PPP/INS紧组合


time delay single and multi sensor fusion framework based on an EKF


Fusing GNSS and wheel measurements based on FAST-LIO and IKFOM


Ultra-Fusion: A Resilient Tightly-Coupled Multi-Sensor Fusion SLAM Framework under Sensor Degradation and Spatiotemporal Perturbation


Multi-sensor fusion for localization courseware, 深蓝学院, China


[ICCV 2023] SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection


[PAMI'23] TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving; [CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving


Lvio-Fusion: A Self-adaptive Multi-sensor Fusion SLAM Framework Using Actor-critic Method (IROS 2021)


A Multi-sensor Fusion Odometry via Smoothing and Mapping.


Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation (ICCV 2021)


[RA-L 2024] Fusing deep dense bundle adjustment (DBA) with multi-sensor factor graph optimization.


Multi-Sensor Fusion SLAM Based on A-LOAM.


localization and mapping based on multiple sensor fusion


Resources Related to Event-based Vision | Event Cameras | DVS


Code for "Continuous-time Intensity Estimation Using Event Cameras" (ACCV, 2018)


interfaces and algorithms for event based cameras, lidars, and actuators


This repository accompanies our ICCP 2017 publication "Real-Time Panoramic Tracking for Event Cameras"


Event camera (DVS, Spike) based Papers Published on Top International Conference


This repository provides software to our publication "Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation", BMVC 2016


A set of algorithms to work with DAVIS/DVS event-based cameras


EVDodgeNet: Deep Dynamic Obstacle Dodging with event cameras


[IEEE TCYB 2023] The first large-scale tracking dataset by fusing RGB and Event cameras.


Official implementation of AISY 2022 paper MC-EMVS (Multi-Camera Event-based Multi-View Stereo)


Hardware Acceleration of Neural Networks for Event Camera-Based Object Detection on SoC FPGAs


Global optical flow estimation with an event camera using contrast maximization (EVIS course at TU Berlin)


Calculate full image from event based camera (dynamic vision sensor)


Here we collect papers for DVS4PD(DVS/Event-based Camera for Pedestrian Detection) from past 10 years.


use event-based camera to get sonification!


Neuromorphic Bird Classifier Desktop App (NeuroBCDA) bundled with Live Event Camera Simulator


Dynamic Vision Sensor Application (Event Based Camera detection)


Partial re-implementation of Simultaneous Mosaicing and Tracking with an Event Camera, BMVC 2014 (EVIS course at TU Berlin)


This repository provides tools for calibrating single or multiple Inivation event cameras.


Docker Image of the Intel Lava Neuromorphic Computing Framework


Repository collecting papers about neuromorphic hardware, such as ASIC and FPGA implementations of SNNs and stuff.


Open source SDK to create applications leveraging event-based vision hardware equipment


Notebooks and code for Neuromorphic Hardware Workshop at ISFPGA 2024.


Tools for manipulating .aedat files (timestamped address-event data from neuromorphic hardware), in Matlab and Python


Python PyNN code demonstrating classification using SpiNNaker neuromorphic hardware. This work was developed at the University of Sussex under funding from the Human Brain Project (HBP).


A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.


Notebooks for Hardware-Aware Training of Spiking Neural Networks. Open-Source Neuromorphic Circuit Design Tutorial at ESSCIRC 2023.


A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.


some of our simulation codes for : A Low-Cost High-Speed Neuromorphic Hardware Based on Spiking Neural Network


PyTorch and Loihi implementation of the Spiking Neural Network for decoding EEG on Neuromorphic Hardware


NeMo -  A hardware agnostic neuromorphic processor simulation model built on ROSS


Neuromorphic hardware implementaion for Master thesis


Self‑calibrated neuromorphic hyperspectral imaging pipeline for event cameras with diffractive  illumination. Includes end‑to‑end tools for RAW segmentation, multi‑window time‑warping compensation, spectral visualization, and hardware contr


My name is Fang Biao. I'm currently pursuing my Master degree with the college of Computer Science and Engineering, Si Chuan University, Cheng Du, China. For more informantion about me and my research, you can go to [my homepage](https://gi


Code for LENS, fully neuromorphic place recognition integrating sensors, hardware, and algorithms for robotic localization.


SNN implementation of the Fourier transform in neuromorphic hardware


Code to accompany the paper "Solving Sparse Finite Element Problems on Neuromorphic Hardware"


Neuromorphic architectures are hardware architectures that use the biologically inspired neural functions as the basis of operation. Information processing based on spiking neuron architectures have caught considerable attention in recent y


Dynex is a next-generation platform for neuromorphic computing based on a new flexible blockchain protocol. It is designed for the development of software applications and algorithms that utilize neuromorphic hardware and are capable of acc


Decoding of 3D Reach and Grasp Movements from Non-invasive EEG Signals using SpiNNaker Neuromorphic Hardware


OpenCL-based simulator for Nengo neural models


MPI backend for the nengo neural simulator.


Demo code of neural simulators for Google Colab


Example of how to call the Nengo neural simulator from C++


Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator


aver: active vision models, built with the Nengo neural simulator


The Nengo neural simulator, implemented in Java.


An OpenCL-accelerated backend for population surrogate simulation with the Nengo 2.0 neural simulator


Workshop on simulating and collaborating on neural architectures built with the Nengo simulator.


This is the repo which describes the Master Thesis work published at [] in which a communication scheme between the Neural Network Development Software, Nengo and the Flight Simulator, X-Plane are coonected for MIL simulations through ROS.


Deep and online learning with spiking neural networks in Python


snn implementation for spike-deeplab and spike-fcn


Spiking Neural Networks dependance on initial conditions using snnTorch. The results are presented in my Bachelors Thesis: "The impact of initial parameters on the learning of spiking neural networks".


Spiking ResNet‑18 (snnTorch) for blood‑pressure prediction from PPG. Implements PA‑B residual connections and Densely Additive Connections (Li et al., Rethinking residual connection in training large-scale spiking neural networks). Includes


Перевод с комментариями туториала по  snntorch


Neuromorphic computing is a new technology which uses very-large-scale integration (VLSI) systems containing electrical analogs that mimic neurobiological structures. Such technology can be simulated using the Python framework snnTorch, a d


A differentially private spiking neural network with temporal enhanced pooling


A spiking neural network of the lateral geniculate nucleus developed on snnTorch running on GPU and demonstrating image classification task


Delta Modulation for Spiking Neural Networks (SNN) based on snnTorch's implementation


Demonstrates the conversion of a simple Artificial Neural Network (ANN) into a Spiking Neural Network (SNN) using the snnTorch library.


Spiking neural networks - examples, tests ... snntorch, Lava, ...


Folder for training HAR spiking neural networks with snntorch


A persistent spiking neural network that lives on your desktop — 1,260 neurons, STDP learning, neuromodulators, and an LLM speech layer.


Simple Spiking Neural Network (SNN) using snnTorch for MNIST digit classification


Open-source Benchmark Library for Spiking Neural Network based Time-Series Forecasting.


image segmentation using spiking neural network


Lightweight, Event-Driven Spiking Neural Networks for Edge Devices in JavaScript


Code to reproduce the work in "Optimizing Neural Coding in Spiking Neural Networks for Mammography Image Processing"


Spiking neural network classifier for MNIST using PyTorch and snnTorch.


SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.


A conda-smithy repository for spikingjelly.


Spiking Neural Network for Computer Vision using SpikingJelly framework and Pytorch-Lightning


A paper list of spiking neural networks, including papers, codes, and related websites.  本仓库收集脉冲神经网络相关的顶会顶刊以及CNS论文和代码，正在持续更新中。


Spikingformer: A Key Foundation Model for Spiking Neural Networks (AAAI 2026)


SpikingJelly框架的C++扩展 · The C++ extensions of SpikingJelly


Semantic Segmentation Using Spikingjelly


🔥🔥🔥A collection of some awesome public SNN(Spiking Neural Network) projects.


This repository contains files related to the training of SNNs for object recognition using SpikingJelly library and PyTorch.


A new dendritic computing package based on pytorch and spikingjelly. Draw inspiration from the nonlinear nature of biological dendritic neurons!


A lite and essential version of spikingjelly (https://github.com/fangwei123456/spikingjelly)


SpikingJelly + LIAF Neurons with Lightning


Extensions for the SpikingJelly library.


A library for scientific machine learning and physics-informed learning


Implementation of Physics-Informed Neural Networks for Computational Mechanics based on the DeepXDE package.


Physics-Informed Neural Networks: Forward/Inverse Modeling of Partial Differential Equations


Contains ZCS integrated version of the DeepXDE


using deepxde to predict covid-19s SIR coefficients and covid spread


Horovod acceleration of the DeepXDE library


Examples of Physics-Informed Neural Networks (PINNs), implemented with PyTorch and DeepXDE.


My undergraduate end of studies project / Mon projet de fin d'etudes de Licence SOLVING DIFFERENTIAL EQUATIONS USING THE DEEP LEARNING LIBRARY DEEPXDE / RESOLUTION DES EQUATIONS DIFFERENTIELLES EN UTLISANT LA LIBRAIRIE D'APPRENTISSAGE PROFO


Using NVIDIA modulus for airfoil optimizations at different angles.


CFD simulation performed using Physics based AI models (Nvidia modulus)


During 2024, I was heavily invested in NVIDIA's Modulus now known as PhysicsNemo. This repository is dedicated to my vision I had in 2024 of combining computer vision with physics. To capture the seen and unseen.


Post-Training Quantization for MeshGraphNet Physics-Based ML Model: Cardiovascular Flow Simulation Implementation


Physics-Informed Neural Network (PINN) examples for classical ODE systems, including Newton’s Law of Cooling and a three-mass spring system, implemented with PyTorch and NVIDIA Modulus.


Physics-informed neural network solving Maxwell's equations in ICP reactors. Hard BC ansatz, transfer learning across geometries, autograd sensitivity maps. 1700× faster than FEM. Built with NVIDIA Modulus + PyTorch.


Geometry-Aware Fourier Neural Operator (Geo-FNO)


DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia


[ICLR 2023] Factorized Fourier Neural Operators


A small research project about advanced machine learning with neural networks. The Fourier neural operator was implemented here to see if it could predict zero-shot super resolution of fluid flows.


U-FNO - an enhanced Fourier neural operator-based deep-learning model for multiphase flow


Implementation of Fourier Neural Operator using PyTorch


Physics Informed Fourier Neural Operator


Code for "Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains"


Fourier Neural Operators to solve for Allen Cahn PDE equations


Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators


An unofficial implementation of the Fourier Neural Operator in Flax


Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration


Fourier neural operator for real-time simulation of 3D dynamic urban microclimate


Must-read Papers on Physics-Informed Neural Networks.


PyTorch Implementation of Physics-informed Neural Networks


Physics-Informed Neural networks for Advanced modeling


Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation


Physics-informed neural networks package


PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.


A physics-informed neural network for battery SOH estimation


Code accompanying my blog post: So, what is a physics-informed neural network?


Simple PyTorch Implementation of Physics Informed Neural Network (PINN)


physics-informed neural network for elastodynamics problem


Physics-informed neural network for solving fluid dynamics problems


Tutorials for Physics-Informed Neural Networks


Hands-on tutorial for implementing Physics Informed Neural Networks in Pytorch


Neural network based solvers for partial differential equations and inverse problems :milky_way:. Implementation of physics-informed neural networks in pytorch.


Characterizing possible failure modes in physics-informed neural networks.


TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).


Physics Informed Neural Network (PINN) for the wave equation.


Neural decoders for quantum error correction on the 3D Toric code. Includes implementation of TheEND decoder for 2D toric code corrections.


Simplified code base for thesis "Sequential Graph-Based Decoding of the Surface Code using a Hybrid Graph and Recurrent Neural Network Model".  See branch "Thesis" for the code of the thesis.


Neural network decoders for quantum error correction: MLP, LSTM, CNN, GNN, and Transformer vs MWPM on simulated and real IBM hardware surface code circuits.


Source code for ICML26 paper: Rethinking the role of neural decoders in quantum error correction


Autoresearch inspired neural decoder training using Hermes Agent and Stim


Encoder/decoder for quantum error correction schemes on Quantum Optical Neural Networks


Bayesian Neural Network for Quantum Error Correction Decoding


Machine-learning surface-code decoders (random forest, XGBoost, neural network) compared against classical decoders.


Adaptive neural and MWPM decoding framework for latency-constrained surface-code quantum error correction.


Spiking Neural Network based QEC decoders. Working a) with Neuroaikit and b) with Snntorch for NIR exportability.


A quantum error correction decoder with a recurrent, transformer based neural network that works on the surface code.


Experiments with the Toric code: Circuit-level noise simulations and Equivariant Neural Decoder implementation.


A repository aimed at developing a hybrid decoder using a graph neural network together with MWPM.


An end-to-end project on decoding topological quantum error-correcting codes with Graph Neural Networks (GNNs).


Open-source QEC decoders: Pathfinder — a direction-aware neural surface-code decoder (real-time-budget Triton kernel) whose multi-seed ensemble lowers logical error below the prior open-source GNN at operational noise rates — plus a kernel-


✅ Python package for simulation and visualization of quantum error-correction on surface codes. The package provides the ability to inspect the error-correcting code during the decoding process, and tools to benchmark the decoder.


The Toric Code (surface code for quantum error correction) together with the MWPM decoder (need a package) and the union-find decoder programmed in python


Simple and fast quantum error correction architecture simulator for the rotated surface code


Quantum error correction simulation and diagnostics bundle for global education and LMIC labs. Features surface/Steane code integration, syndrome fusion analytics, and automated reproducibility tools.


Fault-tolerant QEC simulator simulating Rotated Surface Code (d=3-11). Implements MWPM decoding, depolarizing/biased noise, and verified threshold analysis with visualizations.


Surface Codes for Error Correction for Quantum Computing


Implementing toric and surface codes for topological quantum error correction


Surface Code Simulation. Explore and simulate quantum error correction using the surface code. This library provides a simple yet powerful framework to experiment with error detection, correction, and logical qubit operations in the quantum


The simulation of Google's quantum error correction code, the surface code.


Source code for the Pinball predecoder, a cryogenic hardware predecoder for surface code quantum error correction decoding under circuit-level noise.


Surfaces Codes - Quantum Error Correction


Interactive Python toolkit for quantum LDPC error correction: circuit builder, real-time belief propagation simulation, surface code and Tanner graph visualization, with Qiskit integration.


Distance-3 rotated surface code simulator using real IBM QPU noise measurements. Connects to FlexiSCD research on dynamic defects.


A research repository for implementing and benchmarking real-time decoders for Surface Codes in Quantum Error Correction. Includes Stim-based simulations, existing decoder implementations, and development of an enhanced real-time decoder.


A surface code quantum error correction simulator


Quantum Error Correction: Surface Code in Q#


Simulation of Surface Codes for Quantum Error Correction


Python Surface Code Quantum Error Correction Decoder


Jupyter notebooks on quantum algorithms: QFT, Shor's factoring, phase estimation, VQE (H₂ & LiH ground states), stabiliser codes, and surface code error correction. Research-level detail, fully runnable with Qiskit 2.x.


Haskell bindings for Stim - Google's high-performance stabilizer circuit simulator.


An MCP server for Stim - quantum stabilizer circuit simulator.


HPC assisted Surface Code threshold simulator powered by Stim, PyMatching, and Sinter for distributed quantum error correction research.


Rotated surface code quantum error correction simulator: Stim + PyMatching threshold curve, D3 lattice animation, RSA-2048 qubit estimator


Modular quantum error correction simulator built in Python and Qiskit, featuring repetition codes, planar surface codes, configurable noise models, MWPM decoding, Monte Carlo benchmarking, and ongoing integration of Stim/PyMatching-based de


The official code repository for the ECCV 2024 accepted paper "Representing Topological Self-Similarity Using Fractal Feature Maps for Accurate Segmentation of Tubular Structures".


Decoding topological quantum error-correcting codes in Python


C++ implementation of the sweep rule decoder for topological quantum codes with boundaries.


message-passing cellular automaton decoders for topological codes


A pure frontend Base-121 color matrix encoder/decoder, featuring built-in Reed-Solomon ECC and a native JS topological vision parser


Resilience of quantum information : Topological study and simulation of decoding of the Toric Code


Official code & data for THE IRON SKELETON. Decoding the 53,000-point Nikkei via Topological Data Analysis.


This project systematically compares classical (analytic) and machine‑learned decoders for finite‑rate topological codes under both simplified Pauli noise and realistic circuit‑level noise. Results will include thresholds, scaling laws, run


The Surface Code Stabilizer Group as the Topological col(F)/ker(F) of a Logical Qubit, the Below-Threshold Λ Suppression as the Sherman–Morrison Exponential Decay of the Logical Error Rate, the Real-Time Decoder as the Fisher-Information-Op


[MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration


# TurboQuant v3 (INT4 + AWQ + Protected Channels + Low-Rank) This notebook demonstrates a **TurboQuant-like** quantization algorithm: - Group-wise INT4 quantization - Activation-aware scaling (AWQ-style) - Protected FP16 channels - Optional


A self-contained end-to-end quantization framework that currently supports AWQ (Activation-aware Weight Quantization) and Qwen3 models.


Activation-Aware Ternary Quantization for LLMs


Activation-Aware Weight Quantization for LLMs


MLSys Project: Activation-aware Weight Quantization


Rotation-aware quantization for LLM activations on Apple Silicon, featuring Hadamard transforms, Lloyd-Max quantization, and native PyTorch integration


Boundary-Aware Activation Scaling for Quantization-Aware Training of Small-Object Segmentation


AWQ Model Quantization: Compress LLM weights from FP16 to INT4 using Activation-aware Weight Quantization


Activation-aware test-time quantization framework to compress and accelerate LLM inference without retraining.


valuate LUT-based sigmoid/tanh approximations in a minimal LSTM classifier, with optional quantization-aware training.


Course project on LLMs quantization for intro2ML ran by NJU-SIST. Reproduction of AWQ activation-aware weight quantization for LLM.


LLM Compression : Improved AWQ(Activation-aware Weight Quantization) with extra scaling (2024 Fall graduation project)


This repository contains the reproduction of the paper "AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration".


Activation-aware weight quantization for Mixture-of-Experts models. Profiles per-expert routing frequency in OLMoE-1B-7B and allocates bit precision accordingly — keeping high-traffic experts at INT8 while pushing rarely-activated experts t


4 bit Activation aware Weight Quantization (AWQ) pipeline and CI benchmarking tool optimized for Solar 10.7B high throughput serving.


This repo tries to implement Activation-aware Weight Quantization for LLM Compression and Acceleration from scratch and apply it on small LLMs and assess performance degradation


Accessible large language models via k-bit quantization for PyTorch.


Library for 8-bit optimizers and quantization routines.


Windows compile of bitsandbytes for use in text-generation-webui.


Convenient wrapper for fine-tuning and inference of Large Language Models (LLMs) with several quantization techniques (GTPQ, bitsandbytes)


4bit bitsandbytes quants of the best 7B vlms


A comprehensive systems programming toolkit implementing low-level concepts in C, from memory management to OS internals. Features practical implementations of computer architecture concepts with a focus on performance and hardware interact


A repository containing prebuilt versions of the bitsandbytes library for Windows


A conda-smithy repository for bitsandbytes.


Offical website for the club built with the Next.js framework


Transformers with bitsandbytes on SageMaker


Faster Pytorch bitsandbytes 4bit fp4 nn.Linear ops


Estimate whether a Hugging Face model fits and fine-tunes on your local GPU.


Fork of BnB for updating Metal and ROCm implementations from.


Official repository of my book "A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face"


An extension to enable performance acceleration for bitsandbytes on Intel platforms.


A high-throughput and memory-efficient inference and serving engine for LLMs


TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports high-performing optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create P


OpenLake is a high performance storage engine for efficient LLM inference and GPU Training


Efficient LLM Inference over Long Sequences


This library empowers users to smoothly port pretrained models and checkpoints on the HuggingFace (HF) hub (developed using HF transformers library) into inference-ready formats that run efficiently on Qualcomm Cloud AI 100 accelerators.


Efficient LLM inference on Slurm clusters.


[ICML 2024] Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference


An innovative library for efficient LLM inference via low-bit quantization


Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity


An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).


NobodyWho is an inference engine that lets you run LLMs locally and efficiently on any device.


[ICLR 2025] DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads


[ICLR 2026] ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference


DFloat11 [NeurIPS '25]: Lossless Compression of LLMs and DiTs for Efficient GPU Inference


An endpoint server for efficiently serving quantized open-source LLMs for code.


EFFICIENT AND OPTIMIZED TOKENIZER ENGINE FOR LLM INFERENCE SERVING


Inferflow is an efficient and highly configurable inference engine for large language models (LLMs).


GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM


A flexible serving framework that delivers efficient and fault-tolerant LLM inference for clustered deployments.


An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.


Code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers".


LLM model quantization (compression) toolkit with HW acceleration support for Nvidia, AMD, Intel GPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.


Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.


SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; widely-used model compression techniques on PyTorch, TensorFlow, and ONNX Runtime


Efficient non-uniform quantization with GPTQ for GGUF


A general 2-8 bits quantization toolbox with GPTQ/AWQ/HQQ/VPTQ, and export to onnx/onnx-runtime easily.


记录量化LLM中的总结。


Another ChatGLM2 implementation for GPTQ quantization


A guide about how to use GPTQ models with langchain


JAX implementation of GPTQ quantization algorithm


cli tool to quantize gguf, gptq, awq, hqq and exl2 models


A GPTQ‑quantized version of Eric Hartford’s Dolphin 2.5 Mixtral 8x7B model, fine‑tuned for coding and conversational tasks. <metadata> gpu: A100 | collections: ["vLLM","GPTQ"] </metadata>


Code for MatGPTQ: Accurate and Efficient Post-Training Matryoshka Quantization


Chat to LLaMa 2 that also provides responses with reference documents over vector database. Locally available model using GPTQ 4bit quantization.


A GPTQ-quantized variant of the Mixtral 8x7B model, fine-tuned for efficient text generation and conversational applications. <metadata> gpu: A100 | collections: ["vLLM","GPTQ"] </metadata>


AutoAPTQ: Integrated Quantization Framework (APTQ, GPTQ, AWQ)


Fine-tuning, DPO, RLHF, RLAIF on LLMs - Qwen3, Zephyr 7B GPTQ with 4-Bit Quantization, Mistral-7B-GPTQ


Official Repository for "The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm" (ICLR 2026)


[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA,  BLOOM, Vicuna, Baichuan, TinyLlama, etc.


OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM


Neural Network Compression Framework for enhanced OpenVINO™ inference


A powerful model compression framework for LLMs and LVLMs, adapted for NVIDIA GPUs and Huawei Ascend NPUs.


[AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models


REAM: Merging Improves Pruning of Experts in LLMs


A list of awesome papers on compression and acceleration of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs).


LoRP-Locality-Aware Redundancy Pruning for LLM Depth Compression


[NAACL' 25 main] Lillama: Large Language Model Compression via Low-Rank Feature Distillation


Context compiler for TypeScript/JavaScript AI agents. Automatically compiles agent state into optimized LLM payloads with history compression, tool pruning, multi-provider support, and more.


[TMLR] Offical Implementation of ToMoE: ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning


A curated collection of papers and resources on the safety impacts of model compression in Large Language Models (LLMs), highlighting key challenges, risks, and recent advances.


An implementation of the MoDeGPT LLM compression from the ICLR 2025 Conference paper: Modular Decomposition For Large Language Model Compression.


Gateframe Context Manager — OpenCode plugin for layered context management: model-driven compression, supersede pruning, lossless SQLite recall archive, and an emergency compaction governor with auto-resume. Fork of opencode-dcp (AGPL-3.0).


Collaborative Compression for Large-Scale MoE Deployment on Edge


[ICLR2026] When Reasoning Meets Compression: Understanding the Effects of LLMs Compression on Large Reasoning Models. Support interpretation of Qwen, Llama, etc.


INT4/INT5/INT8 and FP16 inference on CPU for RWKV language model


Accelerate LLM with low-bit (FP4 / INT4 / FP8 / INT8) optimizations using ipex-llm


W8A8/W4A8 inference + optimized SDPA on Apple Silicon — unlocking unused INT8 TensorOps in M5 for 1.2–1.9× faster LLM prefill, plus FlashInfer-inspired GQA decode attention for up to 1.6× SDPA speedup, built as MLX custom primitives.


High-performance GPU kernels for LLM inference in OpenAI Triton. Fused RMSNorm, SwiGLU, INT8 GEMM with benchmarks and roofline analysis.


Local LLM inference engine written from scratch in Rust — hand-written AVX-512 assembly kernels, Metal & Vulkan compute shaders. Supports Qwen3, Mistral3, ... Q4/INT8/BF16 quantization.


LLM-Lora-PEFT_accumulate explores optimizations for Large Language Models (LLMs) using PEFT, LORA, and QLORA. Contribute experiments and implementations to enhance LLM efficiency. Join discussions and push the boundaries of LLM optimization


LLM.int8() from First Principles. Most people understand that large language models have billions of parameters, but don't know exactly how we compress them to run on normal GPUs without destroying their intelligence.


Benchmarking INT8 and INT4 quantization methods


🚀 LowMemoryLLM is A lightweight C-based LLM inference engine optimized for memory-constrained environments.  ✨ Features: - 📊 Multiple quantization (INT8/4/2) - 💾 Smart memory management - 🔄 Efficient KV-cache  Built for edge devices and sys


Model quantization techniques for efficient LLM inference. Experiments with INT8, INT4, and mixed-precision quantization.


INT8 16x16 GEMM tile accelerator for LLM FFN inference (FPGA → MPW ready).


Qwen3-8B quantization study across vLLM, TensorRT-LLM, AutoRound, INT8, and MXFP4


Make your own 8-bit linear quantizer that can quantize any LLM, Vision, Audio model to INT8 precision.


Mastering Diverse Domains through World Models


Mastering Atari with Discrete World Models


Unofficial implementation of the Dreamer 4 world model in PyTorch.


World Model based Autonomous Driving Platform in CARLA :car:


[ECCV 2024] DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving


[AAAI 2025] DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation


Pytorch implementation of DreamerV2: MASTERING ATARI WITH DISCRETE WORLD MODELS


WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens


ICLR 2024: SafeDreamer: Safe Reinforcement Learning with World Models


[TMLR'25] "Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents"


Code for TRANSDREAMER: REINFORCEMENT LEARNING WITH TRANSFORMER WORLD MODELS


Pytorch implementation of DreamerV2: Mastering Atari with Discrete World Models, based on the original implementation


DriveDreamer-Policy: A Geometry-Grounded World–Action Model for Unified Generation and Planning


PyTorch implementation of DreamerV3, Mastering Diverse Domains through World Models.


Unofficial implementation of the dreamer v4 world model.


PyTorch implementation of DreamerV3 from "Mastering Diverse Domains with World Models"


A label-efficient Remote Sensing World Model for Multimodal Data Fusion. IEEE TGRS 2025.


A modular PyTorch library designed for learning, training, and deploying world models across various environments.


An assemble of various world model including dreamer v2 and v3


A minimal implementation of DeepMind's Genie world model


Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory


A framework for training world models with virtual environments, complete with annotated environment dataset (RetroAct), exploration agent (AutoExplore Agent), and GenieRedux-G - an implementation of Genie with enhancements


[CVPR 2026] "GenieDrive: Towards Physics-Aware Driving World Model with 4D Occupancy Guided Video Generation"


[ICML 2026] Orienting Latent Actions for Video World Modeling


A comprehensive study of related works and research around Google's Genie 3 model - a new frontier for world models


Hello, World! — A structured study repository for World Models papers (Dreamer, MuZero, JEPA, Genie, Cosmos and beyond).


Pytorch implementation of Genie by deepmind: the coinrun case study at the end of the paper


Replicating the Genie paper. Generative World Model pipeline for multi-agent SSDs (Sequential Social Dilemmas)


An open attempt at reproducing a simplified version of Google's Genie World Model API on a single H100.


implementation of Genie world model in Pytorch


Efficient world model based on google's Genie


Reproducing the evolution of World Models (from Ha et al. (2018) to Dreamer, Genie, etc.) from scratch in PyTorch using Flappy Bird.


Small-scale reproduction of the Genie 1 world model architecture reported in paper "Genie: Generative Interactive Environments".


Integrated AI models with real-world tools via API-driven workflows, strengthening skills in backend scalability and platform engineering.


A consumer-hardware-friendly open implementation inspired by Google DeepMind's Genie World Model, covering data collection and VQ-VAE quantization as the foundation for a latent action model.


A playable, learned dream: Genie-style world model trained on raw CoinRun video (no action labels) + a DreamerV3 agent trained entirely inside the dream, evaluated in the real game


Open source Project Genie. Fine-tuned and served a 1.3b parameter diffusion model, reverse engineered the Suno client to generate background music, and implemented parallel world exploration with websockets + Modal.


PyTorch code and models for V-JEPA self-supervised learning from video.


Implementation of I-JEPA from "Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture"


Official codebase for I-JEPA, the Image-based Joint-Embedding Predictive Architecture. First outlined in the CVPR paper, "Self-supervised learning from images with a joint-embedding predictive architecture."


Curated resources for JEPA (Joint Embedding Predictive Architecture) world models and self-supervised learning


[WACV 2025] Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud


Official implementation of our paper "CNN-JEPA: Self-Supervised Pretraining Convolutional Neural Networks Using Joint Embedding Predictive Architecture"


Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture


[AAAI 2026] AD-L-JEPA: Self-Supervised Representation Learning with Joint Embedding Predictive Architecture for Automotive LiDAR Object Detection


PyTorch implementation of Hierarchical Joint-Embedding Predictive Architecture for multi-scale visual self-supervised learning.


Audio-JEPA is an adaptation of the Joint-Embedding Predictive Architecture (JEPA) for self-supervised audio representation learning. Built upon the I-JEPA paradigm, it uses a Vision Transformer (ViT) backbone to predict latent representatio


recipe for training fully-featured self supervised image jepa models


A simple and efficient implementation of Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (I-JEPA)


Self-supervised learning for BIM element classification using a joint embedding predictive architecture


Self-supervised Pre-training of ECG Classifiers with Joint-Embedding Predictive Architecture


RadJEPA, a self-supervised frame- work built on a Joint Embedding Predictive Architecture that learns without language supervision.


Self-supervised 3D point-cloud representations (PointNet + JEPA). 86% linear probe on ModelNet40, no labels


Source code for "Self-Supervised Representation Learning with a JEPA Framework for Multi-Instrument Music Transcription"


[ICLR ML4RS 2026] Official implementation for the paper "Sat-JEPA-Diff: Bridging Self-Supervised Learning and Generative Diffusion for Remote Sensing", [IEEE GRSL] Official implementation for the paper "Sat-JEPA-Diff: Caption-Guided Zero-RG


Self-Supervised Learning for Time Series Benchmark


Seeing Beyond Words: Self-Supervised Visual Learning for Multimodal Large Language Models


Contrastive Learning of Structured World Models


DayDreamer: World Models for Physical Robot Learning


Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.


Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning


ICCV 2025 | TesserAct: Learning 4D Embodied World Models


DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model. NeurIPS 2024 Spotlight.


Repository for our papers: Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics and Uncertainty-Aware Robotic World Model Makes Offline Model-Based Reinforcement Learning Work on Real Robots


Codes for "AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos"


[ICML'25] The PyTorch implementation of paper: "AdaWorld: Learning Adaptable World Models with Latent Actions".


Learn to build a basic machine learning model from scratch with this repo and tutorial series.


Implementation of several ML models on real-world datasets with detailed explanation in notebooks.


Code for "Learning to Model the World with Language." ICML 2024 Oral.


I used Machine Learning to make a Logistic Regression model using scikit-learn, pandas, numpy, seaborn and matplotlib to predict the results of FIFA 2018 World Cup.


Learning Generative Models of Textured 3D Meshes from Real-World Images, ICCV 2021


Official repository for "RLVR-World: Training World Models with Reinforcement Learning" (NeurIPS 2025), https://arxiv.org/abs/2505.13934


From Vision-Language-Action Models to a Real-World Robot Learning Stack


Open source code for paper "Denoised MDPs: Learning World Models Better Than the World Itself"


Framework and model code for the paper "Language Understanding for Text-based Games using Deep Reinforcement Learning", EMNLP 2015


AirSim-based UAV Foundation Model Learning: Covering Agents, VLN, World Models, etc.


PWM: Policy Learning with Large World Models


Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents


MRMMIA: Membership Inference Attacks on Memory in Chat Agents


HELPER-X: A Unified Instructable Embodied Agent to Tackle Four Interactive Vision-Language Domains with Memory-Augmented Language Models


Are We Ready For An Agent-Native Memory System?


Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs


AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation


Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models


Emergent Language: A Survey and Taxonomy


HDDL 2.1: Towards Defining a Formalism and a Semantics for Temporal HTN Planning


How do language models learn facts? Dynamics, curricula and hallucinations


Provenance-Based Assessment of Plans in Context


Agent Memory Below the Prompt: Persistent Q4 KV Cache for Multi-Agent LLM Inference on Edge Devices


Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery


Large Language Model Evaluation Via Multi AI Agents: Preliminary results


Cognitive Architectures for Language Agents


Demystifying Instruction Mixing for Fine-tuning Large Language Models


WizardLM: Empowering large pre-trained language models to follow complex instructions


Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception


S-Agents: Self-organizing Agents in Open-ended Environments


PB-LLM: Partially Binarized Large Language Models


ARCANE: A Multi-Agent Framework for Interpretable and Configurable Alignment


Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training


Agents: An Open-source Framework for Autonomous Language Agents


Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models


PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications


A Survey of Multi-Agent Deep Reinforcement Learning with Communication


A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations


Context Engineering for Multi-Agent LLM Code Assistants Using Elicit, NotebookLM, ChatGPT, and Claude Code


Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi


TradingAgents: Multi-Agents LLM Financial Trading Framework


Small LLMs Are Weak Tool Learners: A Multi-LLM Agent


PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection


AOAD-MAT: Transformer-based multi-agent deep reinforcement learning model considering agents' order of action decisions


MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaboration


From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models


Understanding Multi-Agent LLM Frameworks: A Unified Benchmark and Experimental Analysis


Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification


Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases


MAEBE: Multi-Agent Emergent Behavior Framework


Project Synapse: A Hierarchical Multi-Agent Framework with Hybrid Memory for Autonomous Resolution of Last-Mile Delivery Disruptions


When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents


Navigating the State of Cognitive Flow: Context-Aware AI Interventions for Effective Reasoning Support


MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning


AIRCC-Clim: a user-friendly tool for generating regional probabilistic climate change scenarios and risk measures


KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph


Grounding LLM Reasoning under Incomplete Graph Evidence


Focus Agent: LLM-Powered Virtual Focus Group


A Plan Reuse Mechanism for LLM-Driven Agent


When Reasoning Beats Scale: A 1.5B Reasoning Model Outranks 13B LLMs as Discriminator


AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning


S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence


Agents at Risk: How Users Unwittingly Undermine LLM Safety


AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of Tool Orchestration


Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3


ROSflight 2.0: Lean ROS 2-Based Autopilot for Unmanned Aerial Vehicles


Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing


Detection of Stealthy Adversaries for Networked Unmanned Aerial Vehicles*


Optimal Trajectory Planning for Cinematography with Multiple Unmanned Aerial Vehicles


Tracking of Micro Unmanned Aerial Vehicles: A Comparative Study


A Semi-distributed Reputation Based Intrusion Detection System for Mobile Adhoc Networks


OpenREALM: Real-time Mapping for Unmanned Aerial Vehicles


A Collision-Free Path Planning Algorithm for Unmanned Aerial Vehicle Delivery


Improving the Detection of Small Oriented Objects in Aerial Images


One-shot Learning for Autonomous Aerial Manipulation


Unmanned Aerial Vehicle Instrumentation for Rapid Aerial Photo System


Intrusion Detection Systems for Networked Unmanned Aerial Vehicles: A Survey


RRT and Velocity Obstacles-based motion planning for Unmanned Aircraft Systems Traffic Management (UTM)


An Autonomous Self-Incremental Learning Approach for Detection of Cyber Attacks on Unmanned Aerial Vehicles (UAVs)


Learn to Accumulate Evidence from All Training Samples: Theory and Practice


A Deep Automotive Radar Detector using the RaDelft Dataset


Oriented object detection in optical remote sensing images using deep learning: a survey


Deep Learning and Computational Physics (Lecture Notes)


A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection


Comprehensive Deployment-Oriented Assessment for Cross-Environment Generalization in Deep Learning-Based mmWave Radar Sensing


BC-IoDT: Blockchain-based Framework for Authentication in Internet of Drone Things


Advanced Drone Swarm Security by Using Blockchain Governance Game


A multitask deep learning model for real-time deployment in embedded systems


Redefining Radar Segmentation: Simultaneous Static-Moving Segmentation and Ego-Motion Estimation using Radar Point Clouds


Monodense Deep Neural Model for Determining Item Price Elasticity


Deep learning observables in computational fluid dynamics


DILIE: Deep Internal Learning for Image Enhancement


MTReD: 3D Reconstruction Dataset for Fly-over Videos of Maritime Domain


Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning


Vessel Re-identification and Activity Detection in Thermal Domain for Maritime Surveillance


SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions


AI Meets Maritime Training: Precision Analytics for Enhanced Safety and Performance


Context-Aware Autoencoders for Anomaly Detection in Maritime Surveillance


Spatial Clustering Approach for Vessel Path Identification


Aerial Maritime Vessel Detection and Identification


Enhancing Maritime Domain Awareness on Inland Waterways: A YOLO-Based Fusion of Satellite and AIS for Vessel Characterization


ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations


OUXT Polaris: Autonomous Navigation System for the 2022 Maritime RobotX Challenge


A Search Strategy and Vessel Detection in Maritime Environment Using Fixed-Wing UAVs


Multi-Scale Correlation-Aware Transformer for Maritime Vessel Re-Identification


The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024


Image and AIS Data Fusion Technique for Maritime Computer Vision Applications


A Tutorial about Random Neural Networks in Supervised Learning


Predicting concentration levels of air pollutants by transfer learning and recurrent neural network


DroNet: Efficient convolutional neural network detector for real-time UAV applications


Masked Conditional Neural Networks for Audio Classification


Sea You Later: Metadata-Guided Long-Term Re-Identification for UAV-Based Multi-Object Tracking


Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking


Integration of the 3D Environment for UAV Onboard Visual Object Tracking


The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory


Fusion of Cellular ISAC and Passive RF Sensing for UAV Detection and Tracking


Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking


Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory


The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks


Repeatedly Energy-Efficient and Fair Service Coverage: UAV Slicing (Proactive UAV Network Slicing for URLLC and Mobile Broadband Service Multiplexing)


Development of a sensory-neural network for medical diagnosing


Changing Data Sources in the Age of Machine Learning for Official Statistics


DOME: Recommendations for supervised machine learning validation in biology


Learning Curves for Decision Making in Supervised Machine Learning: A Survey


Active learning for data streams: a survey


Privacy-preserving machine learning for healthcare: open challenges and future perspectives


Physics-Inspired Interpretability Of Machine Learning Models


A Benchmark Study of Machine Learning Models for Online Fake News Detection


MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning


Emotion in Reinforcement Learning Agents and Robots: A Survey


Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning


MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning


Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory


ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data


Transfer Learning with Pre-trained Conditional Generative Models


Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar


Quantifying the Carbon Emissions of Machine Learning


A Novel Adaptive Deep Network for Building Footprint Segmentation


PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis


Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models


Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation


Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification


Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models


Compute and Energy Consumption Trends in Deep Learning Inference


Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors


DeepCFL: Deep Contextual Features Learning from a Single Image


Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images


Faith in AI can narrow the futures individuals consider


Competing Visions of Ethical AI: A Case Study of OpenAI


Choosing to Be Green: Advancing Green AI via Dynamic Model Selection


Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems


Green AI: Which Programming Language Consumes the Most?


Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction


Towards The Ultimate Brain: Exploring Scientific Discovery with ChatGPT AI


Towards Green AI: Current status and future research


Meaningful human control: actionable properties for AI system development


The New Shape of Search: How Conversational AI Recomposes Information Seeking


Need of AI in Modern Education: in the Eyes of Explainable AI (xAI)


Beyond principlism: Practical strategies for ethical AI use in research practices


DeBiasMe: De-biasing Human-AI Interactions with Metacognitive AIED (AI in Education) Interventions


Exploring utilization of generative AI for research and education in data-driven materials science


Simulation-free Schrödinger bridges via score and flow matching


Flow Matching for Scalable Simulation-Based Inference


Dynamics-Level Watermarking of Flow Matching Models with Random Codes


Diffusion and Flow Matching Models for Tabular Data: A Survey


VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation


Time-averaged velocity and scalar fields of the flow surrounding a group of cylinders


WAM-Flow: Parallel Coarse-to-Fine Motion Planning via Discrete Flow Matching for Autonomous Driving


Improving and generalizing flow-based generative models with minibatch optimal transport


Flow-OPD: On-Policy Distillation for Flow Matching Models


Crowd Flow Modeling of Athletes in Mass Sports Events -- a Macroscopic Approach


Phase-field simulation of core-annular pipe flow


LaTtE-Flow: Layerwise Timestep-Expert Flow-based Transformer


Enriching MRI mean flow data of inclined jets in crossflow with Large Eddy Simulations


Rectified Point Flow: Generic Point Cloud Pose Estimation


Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation


Experimental study of cavitating flow influenced by heat transfer from heated hydrofoil


DNS of vertical plane channel flow with finite-size particles: Voronoi analysis, acceleration statistics and particle-conditioned averaging


Generalization Capability of Deep Learning for Predicting Drag Reduction in Pulsating Turbulent Pipe Flow with Arbitrary Acceleration and Deceleration


Sediment transport under oscillatory flows


Experimental Analysis of Two-Dimensional Pedestrian Flow in front of the Bottleneck


Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow


Language Rectified Flow: Advancing Diffusion Language Generation with Probabilistic Flows


Improving the Training of Rectified Flows


Relative dispersion of particle pairs in turbulent channel flow


A stochastic diffusion process for the Dirichlet distribution


Stochastic inequalities for single-server loss queueing systems


Reframing Generative Models for Physical Systems using Stochastic Interpolants


On a stochastic differential equation arising in a price impact model


Stochastic temporal data upscaling using the generalized k-nearest neighbor algorithm


Physics-aware generative models for turbulent fluid flows through energy-consistent stochastic interpolants


Finite-Time Convergence Analysis of ODE-based Generative Models for Stochastic Interpolants


Generative Modeling via Kernelized Stochastic Interpolants


Generative Super-Resolution of Turbulent Flows via Stochastic Interpolants


Stochastic Threshold Model Trees: A Tree-Based Ensemble Method for Dealing with Extrapolation


Living Without Beth and Craig: Definitions and Interpolants in Description and Modal Logics with Nominals and Role Inclusions


DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection


SimpleBEV: Improved LiDAR-Camera Fusion Architecture for 3D Object Detection


LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection


BiCo-Fusion: Bidirectional Complementary LiDAR-Camera Fusion for Semantic- and Spatial-Aware 3D Object Detection


3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection


3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection


SemanticBEVFusion: Rethink LiDAR-Camera Fusion in Unified Bird's-Eye View Representation for 3D Object Detection


Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers


PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection


SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation


Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection


LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB


RAF: Reliability-Aware Fusion of Camera, LiDAR, and 4D RADAR for Robust 3D Object Detection in Adverse Weather