Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection


MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation


Multi-Sensor Deep Learning for Glacier Mapping


Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning


A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection


RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection


Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey


RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection


TransCAR: Transformer-based Camera-And-Radar Fusion for 3D Object Detection


CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection


CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer


RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization


TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection


RETR: Multi-View Radar Detection Transformer for Indoor Perception


MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion


ALFA: Agglomerative Late Fusion Algorithm for Object Detection


SFGFusion: Surface Fitting Guided 3D Object Detection with 4D Radar and Camera Fusion


Radar Guided Camera Verification for Automatic Emergency Braking Rethinking Object Detection in Radar Camera Fusion


Training a Hidden Markov Model with a Bayesian Spiking Neural Network


NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning


Pre-Synaptic Pool Modification (PSPM): A Supervised Learning Procedure for Spiking Neural Networks


A Digital Machine Learning Algorithm Simulating Spiking Neural Network CoLaNET


Learning from Event Cameras with Sparse Spiking Convolutional Neural Networks


Learning with Spike Synchrony in Spiking Neural Networks


Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks


Towards spiking analog hardware implementation of a trajectory interpolation mechanism for smooth closed-loop control of a spiking robot arm


Analogical and Relational Reasoning with Spiking Neural Networks


Accurate Detection of Spiking Motifs by Learning Heterogeneous Delays of a Spiking Neural Network


Chemical Reaction Networks Learn Better than Spiking Neural Networks


Spiking-DD: Neuromorphic Event Camera based Driver Distraction Detection with Spiking Neural Network


Object Detection with Spiking Neural Networks on Automotive Event Data


An Efficient Spiking Neural Network for Recognizing Gestures with a DVS Camera on the Loihi Neuromorphic Processor


An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel's Loihi


Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging


Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian


Robust trajectory generation for robotic control on the neuromorphic research chip Loihi


The More the Merrier: Running Multiple Neuromorphic Components On-Chip for Robotic Control


Synthetic Biology meets Neuromorphic Computing: Towards a bio-inspired Olfactory Perception System


Solving QUBO on the Loihi 2 Neuromorphic Processor


Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor


Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor


Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi


Event-driven Robust Fitting on Neuromorphic Hardware


Neuromorphic Simulation of Drosophila Melanogaster Brain Connectome on Loihi 2


Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware


CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor


Autonomous Reinforcement Learning Robot Control with Intel's Loihi 2 Neuromorphic Hardware


NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems


NeuroMorse: A Temporally Structured Dataset For Neuromorphic Computing


Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Vision


NEUROSEC: FPGA-Based Neuromorphic Audio Security


Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond


Neuromorphic computing for attitude estimation onboard quadrotors


Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware


Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing


Large Synoptic Survey Telescope: Dark Energy Science Collaboration


Neuromorphic Computing for Low-Power Artificial Intelligence


Neuromorphic hardware as a self-organizing computing system


Neuromorphic Programming: Emerging Directions for Brain-Inspired Hardware


Supervised Learning in Spiking Neural Networks with FORCE Training


SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks


Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks


Strengthening the Training of Convolutional Neural Networks By Using Walsh Matrix


Ensemble and Mixture-of-Experts DeepONets For Operator Learning


MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems


FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning


Operator Learning for Consolidation: An Architectural Comparison for DeepONet Variants


ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines


Operator-valued Kernels for Learning from Functional Response Data


DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs


In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning


On the Similarities of Embeddings in Contrastive Learning


DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators


Multi-scale DeepOnet (Mscale-DeepOnet) for Mitigating Spectral Bias in Learning High Frequency Operators of Oscillatory Functions


Transfer Learning Enhanced DeepONet for Long-Time Prediction of Evolution Equations


Alpha MAML: Adaptive Model-Agnostic Meta-Learning


Optimizing Neural Networks via Koopman Operator Theory


Symmetries on plabic graphs and associated polytopes


Quadratic discrete Fourier transform and mutually unbiased bases


A High-Frequency Uncertainty Principle for the Fourier-Bessel Transform


Sampling Theorem and Discrete Fourier Transform on the Hyperboloid


Fourier multiplier theorems involving type and cotype


$L^{p}$-$L^{q}$ boundedness of Fourier multipliers on Fundamental domains of Lattices in $\mathbb{R}^d$


$L^p-L^q$ local smoothing estimates for the wave equation via $k$-broad Fourier restriction


On a Class of Fourier Integral Operators on Manifolds with Boundary


Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks


Hierarchical Attentional Hybrid Neural Networks for Document Classification


Stability of solutions to stochastic partial differential equations


An inverse problem of Calderon type with partial data


Regularity analysis for stochastic partial differential equations with nonlinear multiplicative trace class noise


Stability of a critical nonlinear neutral delay differential equation


Boundary value problems for elliptic partial differential equations


Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations


Maximum principles for boundary-degenerate second-order linear elliptic differential operators


Pseudodifferential multi-product representation of the solution operator of a parabolic equation


Schauder a priori estimates and regularity of solutions to boundary-degenerate elliptic linear second-order partial differential equations


Rough differential equations driven by signals in Besov spaces


Applications of integral transforms composition method (ITCM) to wave-type singular differential equations and index shift transmutations


Planarly branched rough paths and rough differential equations on homogeneous spaces


Neural Differential Equations for Single Image Super-resolution


Stability and oscillation of linear delay differential equations


Talagrand Concentration Inequalities for Stochastic Partial Differential Equations


Physics-Informed Neural Network for the Transient Diffusivity Equation in Reservoir Engineering


Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow


Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism


Convergence of Physics-Informed Neural Networks for Fully Nonlinear PDE's


Point Neuron Learning: A New Physics-Informed Neural Network Architecture


Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen


Magnetohydrodynamics with Physics Informed Neural Operators


Unified Spatiotemporal Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics


Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective


Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part II: Physics-Informed Neural Networks and Uncertainty Quantification


CO$_2$ sequestration hybrid solver using isogeometric alternating-directions and collocation-based robust variational physics informed neural networks (IGA-ADS-CRVPINN)


Continuous-time quantum error correction


Sparse Mamba Decoder for Quantum Error Correction: Efficient Defect-Centric Processing of Surface Code Syndromes


Entanglement-Assisted Quantum Error-Correcting Codes


Echo-evolution data generation for quantum error mitigation via neural networks


Quantum Error Correction in Quaternionic Hilbert Spaces


Error suppression and error correction in adiabatic quantum computation I: techniques and challenges


Error suppression and error correction in adiabatic quantum computation II: non-equilibrium dynamics


A neural network oracle for quantum nonlocality problems in networks


The END: An Equivariant Neural Decoder for Quantum Error Correction


Thermodynamic Recycling of Algorithmic Failure Branches: Quantum-Computer Demonstration with Quantum Error Correction


Multimodal Machine Learning for Automated ICD Coding


A scalable and real-time neural decoder for topological quantum codes


Kitaev's quantum double model as an error correcting code


Stabilizer Formalism for Operator Algebra Quantum Error Correction


Autonomous Quantum Error Correction in a Four-Photon Kerr Parametric Oscillator


AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration


AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization


AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent


QAM-W: Joint 2D Codebook Quantization for LLM Weights via Hadamard Rotation and Activation-Aware Scaling


YouZhi: Towards High-Concurrency Financial LLMs via Adaptive GQA-to-MLA Transition


Scaling Law for Quantization-Aware Training


Achieving binary weight and activation for LLMs using Post-Training Quantization


Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other


Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners


QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving


EmoShift: Lightweight Activation Steering for Enhanced Emotion-Aware Speech Synthesis


WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More


TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly


RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization


Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights


GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers


The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm


QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts


PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models


Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models


GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation


FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation


Opt-GPTQ: An Optimized GPTQ Combining Sparse Attention and Quantization Techniques


LLM Post-Training: A Deep Dive into Reasoning Large Language Models


Bridging Performance Gaps for ECG Foundation Models: A Post-Training Strategy


Gradient-Based Post-Training Quantization: Challenging the Status Quo


Activation Sensitivity as a Unifying Principle for Post-Training Quantization


ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models


PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization


Large Language Models Lack Understanding of Character Composition of Words


Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models


Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning


Is Self-knowledge and Action Consistent or Not: Investigating Large Language Model's Personality


WizardCoder: Empowering Code Large Language Models with Evol-Instruct


Exploring Advanced Large Language Models with LLMsuite


Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks


Low-bit Quantization of Neural Networks for Efficient Inference


MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization


Low-bit Model Quantization for Deep Neural Networks: A Survey


Searching for Low-Bit Weights in Quantized Neural Networks


Partially Oblivious Neural Network Inference


Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks


Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization


Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision


W4A4 Quantization for Inference on Wan2.2-I2V-A14B


Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design


Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design


Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2


DiLA: Disentangled Latent Action World Models


Operator-on-F complements value-equivalence: a planning-time diagnostic for latent world models


AdaWorld: Learning Adaptable World Models with Latent Actions


Dreaming Of Others: Latent Teammate Modeling In World Models For Multi-Agent Reinforcement Learning


Latent Spatial Memory for Video World Models


Simultaneous Latent Budget Trees for Stratified Classification


Simultaneous Latent Budget Trees for Stratified Classification Cristian Buoncompagni


Olaf-World: Orienting Latent Actions for Video World Modeling


WorldGym: World Model as An Environment for Policy Evaluation


Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report


DREAMer-VXS: A Latent World Model for Sample-Efficient AGV Exploration in Stochastic, Unobserved Environments


Nano World Models: A Minimalist Implementation of Future Video Prediction


Architectural Implications of Graph Neural Networks


Dalorex: A Data-Local Program Execution and Architecture for Memory-bound Applications


Learning State-Space Models of Dynamic Systems from Arbitrary Data using Joint Embedding Predictive Architectures


Towards Assessing Spread in Sets of Software Architecture Designs


LSQCA: Resource-Efficient Load/Store Architecture for Limited-Scale Fault-Tolerant Quantum Computing


A Novel Reconfigurable Architecture of a DSP Processor for Efficient Mapping of DSP Functions using Field Programmable DSP Arrays


BiJEPA: Bi-directional Joint Embedding Predictive Architecture for Symmetric Representation Learning


Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures


VL-JEPA: Joint Embedding Predictive Architecture for Vision-language


Joint Embedding Predictive Architectures Focus on Slow Features


Denoising with a Joint-Embedding Predictive Architecture


Quality Attributes Optimization of Software Architecture: Research Challenges and Directions


Joint-Embedding Predictive Architecture for Self-Supervised Learning of Mask Classification Architecture


Joint Embedding Predictive Architecture for self-supervised pretraining on polymer molecular graphs


Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud


Sora as a World Model? A Complete Survey on Text-to-Video Generation


GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation


Causal Physics Steering in Video World Models via Concept Activation Vectors


Simulating the Visual World with Artificial Intelligence: A Roadmap


AVID: Adapting Video Diffusion Models to World Models


SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix


Assessing Visual Quality of Omnidirectional Videos


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


NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution


S^2VG: 3D Stereoscopic and Spatial Video Generation via Denoising Frame Matrix


What-If World: A Causal Benchmark for General World Models in Embodied Scenarios


Pandora: Towards General World Model with Natural Language Actions and Video States


Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning


One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay


Causal-Paced Deep Reinforcement Learning


Exploring Hierarchy-Aware Inverse Reinforcement Learning


A Tutorial on Meta-Reinforcement Learning


Stabilizing Extreme Q-learning by Maclaurin Expansion


Anderson Acceleration for Reinforcement Learning


Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning


Compression and Localization in Reinforcement Learning for ATARI Games


Accelerating Training in Pommerman with Imitation and Reinforcement Learning


Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks


An Optimistic Perspective on Offline Reinforcement Learning


Maximum Entropy Model-based Reinforcement Learning


Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means


Energy and Policy Considerations for Deep Learning in NLP


Estimating the Carbon Footprint of BLOOM


LLM.int8(): 8-bit Matrix Multiplication for Transformers


QLoRA: Efficient Finetuning of Quantized LLMs


Towards spike-based machine intelligence with neuromorphic computing (Loihi era)


Training Spiking Neural Networks Using Lessons From Deep Learning (snnTorch)


Anti-UAV: A Large-Scale Benchmark for Vision-based UAV Tracking


Physics-Informed Neural Networks (original PINN, Raissi et al.)


Fourier Neural Operator for Parametric PDEs


Suppressing quantum errors by scaling a surface code logical qubit (Google)


ReAct: Synergizing Reasoning and Acting in Language Models


AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation


DSPy: Compiling Declarative LM Calls into Self-Improving Pipelines


Mastering Diverse Domains through World Models (DreamerV3)


Genie: Generative Interactive Environments


Self-Supervised Learning from Images with a JEPA (I-JEPA)


Flow Matching for Generative Modeling (Lipman et al.)


Stochastic Interpolants (Albergo & Vanden-Eijnden)


Lava (lava-nc) neuromorphic framework org


Intel Neuromorphic Research Community (INRC)


IBM Research neuromorphic (TrueNorth/NorthPole)


Human Brain Project / EBRAINS neuromorphic (SpiNNaker, BrainScaleS)


SpiNNaker (Manchester) neuromorphic hardware


MIT HAN Lab (Song Han) - efficient ML / quantization


Neural Magic (LLM Compressor / SparseML)


Caltech AI4Science / neural operators (Anandkumar)


World Labs (Fei-Fei Li, 3D world models)


Song Han (MIT, efficient ML/quantization)


Tim Dettmers (bitsandbytes, QLoRA, 8-bit)


Sasha Luccioni (Hugging Face, ML carbon)


Jason Eshraghian (snnTorch, UC Santa Cruz)


Anima Anandkumar (neural operators, Caltech/NVIDIA)


Oscar Higgott (PyMatching, MWPM decoder)


Yaron Lipman (Flow Matching, Meta/Weizmann)


MLPerf Inference benchmark (incl. Power)


Intel N-DNS Challenge (neuromorphic denoising)


Prophesee event-camera automotive datasets


nuScenes multi-sensor autonomous dataset