id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
693c1973c1766de1ae37a21a3ce21f424a710a7351a90f7399bb13eb280db49d | [
"arxiv",
"semantic_scholar"
] | LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks | Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision. Existing neural architecture search (NAS) methods are typically tailored to a single hardware family, limitin... | [
"Tyler King",
"Timothee Leleu"
] | [
"cs.LG",
"cs.AI",
"cs.AR",
"cs.NE",
"physics.comp-ph"
] | [
"Computer Science",
"Physics"
] | 2026-06-09T00:00:00 | https://arxiv.org/abs/2606.10294 | https://arxiv.org/pdf/2606.10294v1 | 2606.10294 | null | 0 | 0 | false | null | null | 0.35 |
dcbe51675dc51932eb0eca73da81961414ae56661f104f7287e4ded9c3fd431c | [
"arxiv"
] | Surrogate Neural Architecture Codesign Package (SNAC-Pack) | Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a mu... | [
"Jason Weitz",
"Dmitri Demler",
"Benjamin Hawks",
"Aaron Wang",
"Nhan Tran",
"Javier Duarte"
] | [
"cs.LG",
"cs.AI",
"hep-ex"
] | [] | 2026-05-15T00:00:00 | https://arxiv.org/abs/2605.16138 | https://arxiv.org/pdf/2605.16138v2 | 2605.16138 | null | 0 | 0 | true | null | null | 0.65 |
eb7cd2de14decf87d3ead923510817f2d4fba59d1ad9368e3f409d206dd764f8 | [
"arxiv",
"semantic_scholar"
] | Machine Learning for neutron source distributions | In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle list, which is only required during the training stage of the machine learning mode... | [
"Jose Ignacio Robledo",
"Norberto Schmidt",
"Klaus Lieutenant",
"Jingjing Li",
"Stefan Kesselheim",
"Paul Zakalek"
] | [
"physics.ins-det",
"cs.LG",
"physics.comp-ph"
] | [
"Physics",
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.12165 | https://arxiv.org/pdf/2605.12165v1 | 2605.12165 | null | 0 | 0 | false | null | null | 0.35 |
b6a53e25fb9e9bdfc5254fa87f89bb770b97265a3218a34051bc4bc8487327ff | [
"arxiv",
"semantic_scholar"
] | A Transfer Learning Evaluation of Deep Neural Networks for Image Classification | Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by reusing learned weights. This technique is ubiquitous because of its great advantages in achieving high performance while saving training time, memory, and effort in n... | [
"Nermeen Abou Baker",
"Nico Zengeler",
"Uwe Handmann"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11989 | https://arxiv.org/pdf/2605.11989v1 | 2605.11989 | 10.3390/make4010002 | 55 | 0 | false | null | Machine Learning and Knowledge Extraction | 0.55 |
8287b437dcfa56b22608f07f2cf31bf1e5fd3754f0d761076d7b4c708b79a9ac | [
"arxiv",
"semantic_scholar"
] | Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation | Cardiac magnetic resonance (CMR) segmentation underpins quantitative assessment of ventricular structure and function, yet reliable delineation remains difficult due to low tissue contrast, fuzzy boundaries, and inter scan variability. We present CardiacNAS, an evolutionary neural architecture search (NAS) framework th... | [
"Farhana Yasmin",
"Mahade Hasan",
"Haipeng Liu",
"Amjad Ali",
"Ghulam Muhammad",
"Yu Xue"
] | [
"cs.CV",
"cs.AI",
"cs.ET",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-07T00:00:00 | https://arxiv.org/abs/2605.08238 | https://arxiv.org/pdf/2605.08238v1 | 2605.08238 | 10.1109/ICCIT68739.2025.11491084 | 0 | 0 | false | null | F. Yasmin et.al., "Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation," 28th International Conference on Computer and Information Technology (ICCIT), 2025, pp. 2819-2824 | 0.55 |
006b352557a6ee11f7b2d73fad231fb917c094e9b7dcaf97e922c4937a161808 | [
"arxiv",
"semantic_scholar"
] | HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Search | Neural Architecture Search (NAS) has emerged as a powerful framework for automatically discovering neural architectures that balance accuracy and efficiency. However, as AI transitions from static benchmarks to real-world deployment, the traditional focus on hardware-aware efficiency is no longer sufficient. We observe... | [
"Matteo Gambella",
"Fabrizio Pittorino",
"Manuel Roveri"
] | [
"cs.LG",
"cs.AR",
"cs.CL",
"cs.CV",
"cs.NE"
] | [
"Computer Science"
] | 2026-05-03T00:00:00 | https://arxiv.org/abs/2605.04103 | https://arxiv.org/pdf/2605.04103v1 | 2605.04103 | null | 0 | 0 | false | null | null | 0.35 |
5f49ffe1c1482475e7661d40d70089edc14338e3ce4d6796af911a21abbc1634 | [
"arxiv",
"semantic_scholar"
] | G-ICSO-NAS: Shifting Gears between Gradient and Swarm for Robust Neural Architecture Search | Neural Architecture Search (NAS) has become a pivotal technique in automated machine learning. Evolutionary Algorithm (EA)-based methods demonstrate superior search quality but suffer from prohibitive computational costs, while gradient-based approaches like DARTS offer high efficiency but are prone to premature conver... | [
"Xingbang Du",
"Enzhi Zhang",
"Rui Zhong",
"Yang Cao",
"Masaharu Munetomo"
] | [
"cs.NE"
] | [
"Computer Science"
] | 2026-04-01T00:00:00 | https://arxiv.org/abs/2604.00703 | https://arxiv.org/pdf/2604.00703v1 | 2604.00703 | 10.48550/arXiv.2604.00703 | 0 | 0 | false | null | arXiv.org | 0.5282 |
e4af03ec2aafcfa998e4e76fda481799656b899df77dbce3a736bea26b63cb42 | [
"arxiv",
"semantic_scholar"
] | Chemical Reaction Networks Learn Better than Spiking Neural Networks | We mathematically prove that chemical reaction networks without hidden layers can solve tasks for which spiking neural networks require hidden layers. Our proof uses the deterministic mass-action kinetics formulation of chemical reaction networks. Specifically, we prove that a certain reaction network without hidden la... | [
"Sophie Jaffard",
"Ivo F. Sbalzarini"
] | [
"cs.LG",
"cs.AI",
"math.ST",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-03-12T00:00:00 | https://arxiv.org/abs/2603.12060 | https://arxiv.org/pdf/2603.12060v1 | 2603.12060 | 10.48550/arXiv.2603.12060 | 0 | 0 | false | null | arXiv.org | 0.5053 |
a48444f190e46f3a0569c01284568b7645f1bec64a3979486bf6138148698b8e | [
"arxiv",
"semantic_scholar"
] | YOLO-NAS-Bench: A Surrogate Benchmark with Self-Evolving Predictors for YOLO Architecture Search | Neural Architecture Search (NAS) for object detection is severely bottlenecked by high evaluation cost, as fully training each candidate YOLO architecture on COCO demands days of GPU time. Meanwhile, existing NAS benchmarks largely target image classification, leaving the detection community without a comparable benchm... | [
"Zhe Li",
"Xiaoyu Ding",
"Jiaxin Zheng",
"Yongtao Wang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-03-10T00:00:00 | https://arxiv.org/abs/2603.09405 | https://arxiv.org/pdf/2603.09405v2 | 2603.09405 | 10.48550/arXiv.2603.09405 | 0 | 0 | true | https://github.com/VDIGPKU/YOLO-NAS-Bench | arXiv.org | 0.7774 |
6e4e2ab1dd36e49b226cda02c0f74faeabd13782dd1cdbf8dd8a91ea55aa7984 | [
"arxiv",
"semantic_scholar"
] | SEval-NAS: A Search-Agnostic Evaluation for Neural Architecture Search | Neural architecture search (NAS) automates the discovery of neural networks that meet specified criteria, yet its evaluation procedures are often hardcoded, limiting the ability to introduce new metrics. This issue is especially pronounced in hardware-aware NAS, where objectives depend on target devices such as edge ha... | [
"Atah Nuh Mih",
"Jianzhou Wang",
"Truong Thanh Hung Nguyen",
"Hung Cao"
] | [
"cs.LG",
"cs.AI",
"cs.NE"
] | [
"Computer Science"
] | 2026-02-17T00:00:00 | https://arxiv.org/abs/2603.00099 | https://arxiv.org/pdf/2603.00099v1 | 2603.00099 | 10.48550/arXiv.2603.00099 | 0 | 0 | true | https://github.com/Analytics-Everywhere-Lab/neural-architecture-search | null | 0.566 |
d790c88703165f0f1bb398150b9539961ceca2247724ee68a9c4f55d5239f3bd | [
"arxiv",
"semantic_scholar"
] | MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning | Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We introduce MerLin, an open-source framework designed as a disco... | [
"Cassandre Notton",
"Benjamin Stott",
"Philippe Schoeb",
"Anthony Walsh",
"GrΓ©goire Leboucher",
"Vincent Espitalier",
"Vassilis Apostolou",
"Louis-FΓ©lix Vigneux",
"Alexia Salavrakos",
"Jean Senellart"
] | [
"cs.LG",
"cs.PL",
"quant-ph"
] | [
"Computer Science",
"Physics"
] | 2026-02-11T00:00:00 | https://arxiv.org/abs/2602.11092 | https://arxiv.org/pdf/2602.11092v2 | 2602.11092 | 10.48550/arXiv.2602.11092 | 1 | 0 | true | null | arXiv.org | 0.7296 |
7acbde9065ce00602bed1dab069b40b5b8ee3cbd2ae2c18d74be3c91068ad8aa | [
"arxiv",
"semantic_scholar"
] | A Multi-objective Evolutionary Algorithm Based on Bi-population with Uniform Sampling for Neural Architecture Search | Neural architecture search (NAS) automates neural network design, improving efficiency over manual approaches. However, efficiently discovering high-performance neural network architectures that simultaneously optimize multiple objectives remains a significant challenge in NAS. Existing methods often suffer from limite... | [
"Yu Xue",
"Pengcheng Jiang",
"Chenchen Zhu",
"Yong Zhang",
"Ran Cheng",
"Kaizhou Gao",
"Dunwei Gong"
] | [
"cs.NE"
] | [
"Medicine",
"Computer Science"
] | 2026-02-09T00:00:00 | https://arxiv.org/abs/2602.08513 | https://arxiv.org/pdf/2602.08513v1 | 2602.08513 | 10.1109/TNNLS.2026.3659508 | 0 | 0 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.4698 |
45e0e4a1dbe615ecef580c52e8ac809e2e28b639c496702107188a92a86178a5 | [
"arxiv",
"semantic_scholar"
] | Automated Machine Learning in Radiomics: A Comparative Evaluation of Performance, Efficiency and Accessibility | Automated machine learning (AutoML) frameworks can lower technical barriers for predictive and prognostic model development in radiomics by enabling researchers without programming expertise to build models. However, their effectiveness in addressing radiomics-specific challenges remains unclear. This study evaluates t... | [
"Jose Lozano-Montoya",
"Emilio Soria-Olivas",
"Almudena Fuster-Matanzo",
"Angel Alberich-Bayarri",
"Ana Jimenez-Pastor"
] | [
"cs.LG"
] | [
"Medicine",
"Computer Science"
] | 2026-01-13T00:00:00 | https://arxiv.org/abs/2601.08334 | https://arxiv.org/pdf/2601.08334v2 | 2601.08334 | 10.2196/91492 | 0 | 0 | true | https://github.com/joselznom/AutoML-Comparison-in-Radiomics | JMIR Formative Research | 0.6782 |
e3289b41c5244d04c95fa76e92f6726fbde06e6739ab81085a07ea6d6c6ab556 | [
"arxiv",
"semantic_scholar"
] | Self-Supervised Neural Architecture Search for Multimodal Deep Neural Networks | Neural architecture search (NAS), which automates the architectural design process of deep neural networks (DNN), has attracted increasing attention. Multimodal DNNs that necessitate feature fusion from multiple modalities benefit from NAS due to their structural complexity; however, constructing an architecture for mu... | [
"Shota Suzuki",
"Satoshi Ono"
] | [
"cs.LG",
"cs.NE"
] | [
"Computer Science"
] | 2025-12-31T00:00:00 | https://arxiv.org/abs/2512.24793 | https://arxiv.org/pdf/2512.24793v1 | 2512.24793 | 10.1587/transinf.2024EDL8018 | 0 | 0 | false | null | IEICE Transactions on Information and Systems, Vol.E108.D, No. 6, pp. 640-643, 2025 | 0.424 |
96faabe62e40cd4bed81ede5a79c721b46ab80b9003ab10d29bcfda5ba33ff7f | [
"arxiv",
"semantic_scholar"
] | Evolutionary Neural Architecture Search with Dual Contrastive Learning | Evolutionary Neural Architecture Search (ENAS) has gained attention for automatically designing neural network architectures. Recent studies use a neural predictor to guide the process, but the high computational costs of gathering training data -- since each label requires fully training an architecture -- make achiev... | [
"Xian-Rong Zhang",
"Yue-Jiao Gong",
"Wei-Neng Chen",
"Jun Zhang"
] | [
"cs.NE",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-23T00:00:00 | https://arxiv.org/abs/2512.20112 | https://arxiv.org/pdf/2512.20112v1 | 2512.20112 | 10.1016/j.asoc.2025.114507 | 1 | 1 | false | null | Applied Soft Computing | 0.4148 |
e40fb6a8f6c51acdf3db11c7f3c030b28e734f7434265bc1e50f53a9fa86cc03 | [
"arxiv",
"semantic_scholar"
] | Deep Learning for Unrelated-Machines Scheduling: Handling Variable Dimensions | Deep learning has been effectively applied to many discrete optimization problems. However, learning-based scheduling on unrelated parallel machines remains particularly difficult to design. Not only do the numbers of jobs and machines vary, but each job-machine pair has a unique processing time, dynamically altering f... | [
"Diego Hitzges",
"Guillaume Sagnol"
] | [
"cs.LG",
"cs.DM"
] | [
"Computer Science"
] | 2025-12-22T00:00:00 | https://arxiv.org/abs/2512.19527 | https://arxiv.org/pdf/2512.19527v1 | 2512.19527 | 10.1109/ICMLA66185.2025.00007 | 0 | 0 | true | https://github.com/DiegoHitzges/Deep-Learning-for-Unrelated-Machines-Scheduling | International Conference on Machine Learning and Applications | 0.6393 |
76b4be66ec43f4116adf98a65cbe7a7555cf16a796ba83d146ca659c97bb01e3 | [
"arxiv",
"semantic_scholar"
] | Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part II: Physics-Informed Neural Networks and Uncertainty Quantification | The integration of physics-based knowledge with machine learning models is increasingly shaping the monitoring, diagnostics, and prognostics of electrical transformers. In this two-part series, the first paper introduced the foundations of Neural Networks (NNs) and their variants for health assessment tasks. This secon... | [
"Jose I. Aizpurua"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-12-20T00:00:00 | https://arxiv.org/abs/2512.22189 | https://arxiv.org/pdf/2512.22189v1 | 2512.22189 | 10.23919/ARWtr66130.2025.11261232 | 0 | 0 | false | null | 8th International Advanced Research Workshop on Transformers (ARWtr), Baiona, Spain, 2025, pp. 95-101 | 0.4114 |
217d7fddcd343d982ff21fc46b86beb28f0ca160280dd66fbbe3012df83e5c7d | [
"arxiv",
"semantic_scholar"
] | DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning | Hyperparameter optimization (HPO), as a central paradigm of AutoML, is crucial for leveraging the full potential of machine learning (ML) models; yet its complexity poses challenges in understanding and debugging the optimization process. We present DeepCAVE, a tool for interactive visualization and analysis, providing... | [
"Sarah Segel",
"Helena Graf",
"Edward Bergman",
"Kristina Thieme",
"Marcel Wever",
"Alexander Tornede",
"Frank Hutter",
"Marius Lindauer"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-12-01T00:00:00 | https://arxiv.org/abs/2512.01810 | https://arxiv.org/pdf/2512.01810v1 | 2512.01810 | 10.48550/arXiv.2512.01810 | 2 | 0 | false | null | Journal of machine learning research | 0.3896 |
ac94675d9b19da7276334499b4755ed74753d0579c6d0a8cca5e4786d62278f6 | [
"arxiv",
"semantic_scholar"
] | Neural Architecture Search for Quantum Autoencoders | In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via qua... | [
"Hibah Agha",
"Samuel Yen-Chi Chen",
"Huan-Hsin Tseng",
"Shinjae Yoo"
] | [
"quant-ph",
"cs.AI",
"cs.LG",
"cs.NE"
] | [
"Computer Science",
"Physics"
] | 2025-11-24T00:00:00 | https://arxiv.org/abs/2511.19246 | https://arxiv.org/pdf/2511.19246v1 | 2511.19246 | 10.1109/QCE65121.2025.00192 | 2 | 0 | false | null | International Conference on Quantum Computing and Engineering | 0.3816 |
d5f224b094c108612e4a7aac91700b23ee35637de5fdb614e6dcbcdf5a7729d2 | [
"arxiv",
"semantic_scholar"
] | OpenCML: End-to-End Framework of Open-world Machine Learning to Learn Unknown Classes Incrementally | Open-world machine learning is an emerging technique in artificial intelligence, where conventional machine learning models often follow closed-world assumptions, which can hinder their ability to retain previously learned knowledge for future tasks. However, automated intelligence systems must learn about novel classe... | [
"Jitendra Parmar",
"Praveen Singh Thakur"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-23T00:00:00 | https://arxiv.org/abs/2511.19491 | https://arxiv.org/pdf/2511.19491v1 | 2511.19491 | 10.48550/arXiv.2511.19491 | 0 | 0 | false | null | arXiv.org | 0.3804 |
546811e29b473938011ead7885178a76de549ad2644f744ed4d8757014f4b095 | [
"arxiv",
"semantic_scholar"
] | Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System | With increasingly sophisticated cybersecurity threats and rising demand for network automation, autonomous cybersecurity mechanisms are becoming critical for securing modern networks. The rapid expansion of Internet of Things (IoT) systems amplifies these challenges, as resource-constrained IoT devices demand scalable ... | [
"Li Yang",
"Abdallah Shami"
] | [
"cs.CR",
"cs.LG",
"cs.NI"
] | [
"Computer Science"
] | 2025-11-11T00:00:00 | https://arxiv.org/abs/2511.08491 | https://arxiv.org/pdf/2511.08491v1 | 2511.08491 | 10.1109/TMLCN.2025.3631379 | 5 | 0 | true | https://github.com/Western-OC2-Lab/Multi-Objective-Optimization-AutoML-based-Intrusion-Detection-System | IEEE Transactions on Machine Learning in Communications and Networking | 0.5667 |
c6964acade295d4abc9b4321caa40614d63038dcfb56ad1232ea1490d8b55c91 | [
"arxiv",
"semantic_scholar"
] | Group Averaging for Physics Applications: Accuracy Improvements at Zero Training Cost | Many machine learning tasks in the natural sciences are precisely equivariant to particular symmetries. Nonetheless, equivariant methods are often not employed, perhaps because training is perceived to be challenging, or the symmetry is expected to be learned, or equivariant implementations are seen as hard to build. G... | [
"Valentino F. Foit",
"David W. Hogg",
"Soledad Villar"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-11-11T00:00:00 | https://arxiv.org/abs/2511.09573 | https://arxiv.org/pdf/2511.09573v1 | 2511.09573 | 10.48550/arXiv.2511.09573 | 0 | 0 | false | null | arXiv.org | 0.3667 |
24dce3c2adbb9c65aaa655e01bffd3d5f34dc85f2ff8b833915c0620a1da29c2 | [
"arxiv",
"semantic_scholar"
] | Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration | Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality predictions. Earth Observation (EO) represents a quintessential domain for multi-modal data analysis, wherein diverse remote sensors collect dat... | [
"Francisco Mena",
"Dino Ienco",
"Cassio F. Dantas",
"Roberto Interdonato",
"Andreas Dengel"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-10-22T00:00:00 | https://arxiv.org/abs/2510.19579 | https://arxiv.org/pdf/2510.19579v1 | 2510.19579 | 10.1007/s10994-025-06903-0 | 3 | 0 | false | null | Machine-mediated learning | 0.3438 |
014390681dbeb83e7eb96d8ffe29750fb324216cecbd4383087972cf7fdd7c78 | [
"arxiv",
"semantic_scholar"
] | Automated Machine Learning for Unsupervised Tabular Tasks | In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsupervised machine learning(ML) tasks such as outlier detection and clustering. Our intuition behind this work is that a machine learning pipeline... | [
"Prabhant Singh",
"Pieter Gijsbers",
"Elif Ceren Gok Yildirim",
"Murat Onur Yildirim",
"Joaquin Vanschoren"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-08T00:00:00 | https://arxiv.org/abs/2510.07569 | https://arxiv.org/pdf/2510.07569v2 | 2510.07569 | 10.1007/s10994-025-06984-x | 0 | 0 | false | null | Machine-mediated learning | 0.3277 |
4b8d439cf0a1c1dc6ea786ff2b3b76d42b27e32505b4ab37095a7d3953c56ed0 | [
"arxiv",
"semantic_scholar"
] | LLM-NAS: LLM-driven Hardware-Aware Neural Architecture Search | Hardware-Aware Neural Architecture Search (HW-NAS) requires joint optimization of accuracy and latency under device constraints. Traditional supernet-based methods require multiple GPU days per dataset. Large Language Model (LLM)-driven approaches avoid training a large supernet and can provide quick feedback, but we o... | [
"Hengyi Zhu",
"Grace Li Zhang",
"Shaoyi Huang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-01T00:00:00 | https://arxiv.org/abs/2510.01472 | https://arxiv.org/pdf/2510.01472v4 | 2510.01472 | null | 0 | 0 | false | null | null | 0.2034 |
0aecab3a49deaf3540a05658293f6863e15a5b6fb9b1bd03b41dc6c8ab97eae8 | [
"arxiv",
"semantic_scholar"
] | CoLLM-NAS: Collaborative Large Language Models for Efficient Knowledge-Guided Neural Architecture Search | The integration of Large Language Models (LLMs) with Neural Architecture Search (NAS) has introduced new possibilities for automating the design of neural architectures. However, most existing methods face critical limitations, including architectural invalidity, computational inefficiency, and inferior performance com... | [
"Zhe Li",
"Zhiwei Lin",
"Yongtao Wang"
] | [
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-09-30T00:00:00 | https://arxiv.org/abs/2509.26037 | https://arxiv.org/pdf/2509.26037v2 | 2509.26037 | 10.48550/arXiv.2509.26037 | 3 | 0 | false | null | arXiv.org | 0.3185 |
4fa018b5913bc4e35e237ee3e3593010d7fbd46c9c67a67a1dd474b00a357bdf | [
"arxiv",
"semantic_scholar"
] | Active Learning for Machine Learning Driven Molecular Dynamics | Machine-learned coarse-grained (CG) potentials are fast, but degrade over time when simulations reach under-sampled bio-molecular conformations, and generating widespread all-atom (AA) data to combat this is computationally infeasible. We propose a novel active learning (AL) framework for CG neural network potentials i... | [
"Kevin Bachelor",
"Sanya Murdeshwar",
"Daniel Sabo",
"Razvan Marinescu"
] | [
"cs.LG",
"physics.atm-clus"
] | [
"Computer Science",
"Physics"
] | 2025-09-21T00:00:00 | https://arxiv.org/abs/2509.17208 | https://arxiv.org/pdf/2509.17208v3 | 2509.17208 | 10.48550/arXiv.2509.17208 | 0 | 0 | false | null | arXiv.org | 0.3082 |
279808ea1948168957485f62fec7a87d0e92544e8a8a02ce71c57b3d740caec1 | [
"arxiv",
"semantic_scholar"
] | Quantum Architecture Search for Solving Quantum Machine Learning Tasks | Quantum computing leverages quantum mechanics to address computational problems in ways that differ fundamentally from classical approaches. While current quantum hardware remains error-prone and limited in scale, Variational Quantum Circuits offer a noise-resilient framework suitable for today's devices. The performan... | [
"Michael KΓΆlle",
"Simon Salfer",
"Tobias Rohe",
"Philipp Altmann",
"Claudia Linnhoff-Popien"
] | [
"quant-ph",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Physics"
] | 2025-09-14T00:00:00 | https://arxiv.org/abs/2509.11198 | https://arxiv.org/pdf/2509.11198v1 | 2509.11198 | 10.48550/arXiv.2509.11198 | 0 | 0 | false | null | International Conference on Agents and Artificial Intelligence | 0.3002 |
964fb412be437914c7fa790ee74f680456650d4f4cb6df184347e3b1b007478c | [
"arxiv",
"semantic_scholar"
] | Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning | We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedforward structure with cosine activation functions to learn the frequencies, amplitudes, and phase shifts of the series as trainable paramete... | [
"Mominul Rubel",
"Adam Meyers",
"Gabriel Nicolosi"
] | [
"cs.LG",
"math.OC"
] | [
"Computer Science",
"Mathematics"
] | 2025-09-10T00:00:00 | https://arxiv.org/abs/2509.08759 | https://arxiv.org/pdf/2509.08759v3 | 2509.08759 | 10.48550/arXiv.2509.08759 | 1 | 0 | false | null | Transactions on Machine Learning Research, December 2025 | 0.2956 |
4fb49551610033567729c2c9e7db06eba624c933b0ca820d827fc20b42361306 | [
"arxiv",
"semantic_scholar"
] | OptiProxy-NAS: Optimization Proxy based End-to-End Neural Architecture Search | Neural architecture search (NAS) is a hard computationally expensive optimization problem with a discrete, vast, and spiky search space. One of the key research efforts dedicated to this space focuses on accelerating NAS via certain proxy evaluations of neural architectures. Different from the prevalent predictor-based... | [
"Bo Lyu",
"Yu Cui",
"Tuo Shi",
"Ke Li"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-06T00:00:00 | https://arxiv.org/abs/2509.05656 | https://arxiv.org/pdf/2509.05656v1 | 2509.05656 | 10.48550/arXiv.2509.05656 | 0 | 0 | false | null | arXiv.org | 0.291 |
cd00a6d111083e0247bc0052da782dc7c373e8b1fdb35c6d399e5a3d8aa26fb8 | [
"arxiv",
"semantic_scholar"
] | P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials | Ensemble method is considered the gold standard for uncertainty quantification (UQ) in machine learning interatomic potentials (MLIPs). However, their high computational cost can limit its practicality. Alternative techniques, such as Monte Carlo dropout and deep kernel learning, have been proposed to improve computati... | [
"Shih-Peng Huang",
"Nontawat Charoenphakdee",
"Yuta Tsuboi",
"Yong-Bin Zhuang",
"Wenwen Li"
] | [
"cs.LG",
"cond-mat.mtrl-sci"
] | [
"Computer Science",
"Physics"
] | 2025-09-03T00:00:00 | https://arxiv.org/abs/2509.02927 | https://arxiv.org/pdf/2509.02927v2 | 2509.02927 | null | 0 | 0 | false | null | null | 0.183 |
925621ad2f75055c0e13214a0c6a4927b140bf9f4df4427650851e72e6d8d93f | [
"arxiv",
"semantic_scholar"
] | Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It | The success of federated learning (FL) ultimately depends on how strategic participants behave under partial observability, yet most formulations still treat FL as a static optimization problem. We instead view FL deployments as governed strategic systems and develop an analytical framework that separates welfare-impro... | [
"Dongseok Kim",
"Hyoungsun Choi",
"Mohamed Jismy Aashik Rasool",
"Gisung Oh"
] | [
"cs.LG",
"cs.GT",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-09-02T00:00:00 | https://arxiv.org/abs/2509.02391 | https://arxiv.org/pdf/2509.02391v3 | 2509.02391 | 10.48550/arXiv.2509.02391 | 0 | 0 | false | null | Transactions on Machine Learning Research, 2026 | 0.2865 |
aef7d374d50b25f79f2e7b5daf2ebd0742cef0140203548a928fd15789fd9a3a | [
"arxiv",
"semantic_scholar"
] | SAR-NAS: Lightweight SAR Object Detection with Neural Architecture Search | Synthetic Aperture Radar (SAR) object detection faces significant challenges from speckle noise, small target ambiguities, and on-board computational constraints. While existing approaches predominantly focus on SAR-specific architectural modifications, this paper explores the application of the existing lightweight ob... | [
"Xinyi Yu",
"Zhiwei Lin",
"Yongtao Wang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-09-01T00:00:00 | https://arxiv.org/abs/2509.01279 | https://arxiv.org/pdf/2509.01279v1 | 2509.01279 | 10.48550/arXiv.2509.01279 | 0 | 0 | false | null | Chinese Conference on Pattern Recognition and Computer Vision | 0.2853 |
32d2988dc8d5cccf506879a4a6eba75b3ef645d282684800b760c7664a41b149 | [
"arxiv",
"semantic_scholar"
] | AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data | Medical datasets are typically affected by issues such as missing values, class imbalance, a heterogeneous feature types, and a high number of features versus a relatively small number of samples, preventing machine learning models from obtaining proper results in classification and regression tasks. This paper introdu... | [
"Riccardo Francia",
"Maurizio Leone",
"Giorgio Leonardi",
"Stefania Montani",
"Marzio Pennisi",
"Manuel Striani",
"Sandra D'Alfonso"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-08-04T00:00:00 | https://arxiv.org/abs/2508.02625 | https://arxiv.org/pdf/2508.02625v1 | 2508.02625 | 10.1109/BIBM66473.2025.11356268 | 0 | 0 | false | null | IEEE International Conference on Bioinformatics and Biomedicine | 0.2532 |
ccf095d5c751ca10067485de119b83395804dfb2fd55d297e20f053acd71eeb9 | [
"arxiv",
"semantic_scholar"
] | Neural Architecture Search with Mixed Bio-inspired Learning Rules | Bio-inspired neural networks are attractive for their adversarial robustness, energy frugality, and closer alignment with cortical physiology, yet they often lag behind back-propagation (BP) based models in accuracy and ability to scale. We show that allowing the use of different bio-inspired learning rules in differen... | [
"Imane Hamzaoui",
"Riyadh Baghdadi"
] | [
"cs.NE",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-07-17T00:00:00 | https://arxiv.org/abs/2507.13485 | https://arxiv.org/pdf/2507.13485v1 | 2507.13485 | 10.48550/arXiv.2507.13485 | 0 | 0 | false | null | European Conference on Artificial Intelligence | 0.2326 |
a6f139a88eaf7f87941db615fb69f77f10de3a147c36a4a52a5b9198088f25c9 | [
"arxiv",
"semantic_scholar"
] | DASViT: Differentiable Architecture Search for Vision Transformer | Designing effective neural networks is a cornerstone of deep learning, and Neural Architecture Search (NAS) has emerged as a powerful tool for automating this process. Among the existing NAS approaches, Differentiable Architecture Search (DARTS) has gained prominence for its efficiency and ease of use, inspiring numero... | [
"Pengjin Wu",
"Ferrante Neri",
"Zhenhua Feng"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2025-07-17T00:00:00 | https://arxiv.org/abs/2507.13079 | https://arxiv.org/pdf/2507.13079v1 | 2507.13079 | 10.1109/IJCNN64981.2025.11229286 | 0 | 0 | false | null | IEEE International Joint Conference on Neural Network | 0.2326 |
a4cb0b70b24b402d7546f3c9179fccf00cf87b055777e92d42484473cb17dad8 | [
"arxiv",
"semantic_scholar"
] | On the Similarities of Embeddings in Contrastive Learning | Contrastive learning operates on a simple yet effective principle: Embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. In this paper, we propose a unified framework for understanding contrastive learning through the lens of cosine similarity, and present two key theoretical... | [
"Chungpa Lee",
"Sehee Lim",
"Kibok Lee",
"Jy-yong Sohn"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-06-11T00:00:00 | https://arxiv.org/abs/2506.09781 | https://arxiv.org/pdf/2506.09781v2 | 2506.09781 | 10.48550/arXiv.2506.09781 | 2 | 0 | false | null | International Conference on Machine Learning | 0.1914 |
61e270824e2426e65dd34467e8f3568f1547bb84f7fbcb85433751893b30923e | [
"arxiv",
"semantic_scholar"
] | CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems | In this paper, we propose the CrossNAS framework, an automated approach for exploring a vast, multidimensional search space that spans various design abstraction layers-circuits, architecture, and systems-to optimize the deployment of machine learning workloads on analog processing-in-memory (PIM) systems. CrossNAS lev... | [
"Md Hasibul Amin",
"Mohammadreza Mohammadi",
"Jason D. Bakos",
"Ramtin Zand"
] | [
"cs.ET",
"cs.AR",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-28T00:00:00 | https://arxiv.org/abs/2505.22868 | https://arxiv.org/pdf/2505.22868v1 | 2505.22868 | 10.1145/3716368.3735178 | 1 | 0 | false | null | ACM Great Lakes Symposium on VLSI | 0.1753 |
fd254efb1bf0f3d7f39e441cce8586c91a4c54529e5cd8531c27a281506ab490 | [
"arxiv",
"semantic_scholar"
] | Efficient Training of Neural SDEs Using Stochastic Optimal Control | We present a hierarchical, control theory inspired method for variational inference (VI) for neural stochastic differential equations (SDEs). While VI for neural SDEs is a promising avenue for uncertainty-aware reasoning in time-series, it is computationally challenging due to the iterative nature of maximizing the ELB... | [
"Rembert Daems",
"Manfred Opper",
"Guillaume Crevecoeur",
"Tolga Birdal"
] | [
"cs.LG",
"cs.AI",
"math.PR"
] | [
"Computer Science",
"Mathematics"
] | 2025-05-22T00:00:00 | https://arxiv.org/abs/2505.17150 | https://arxiv.org/pdf/2505.17150v1 | 2505.17150 | 10.14428/esann/2025.es2025-182 | 3 | 0 | false | null | The European Symposium on Artificial Neural Networks | 0.1684 |
836a547cb0622e6e0980e7fc843566b41311fd0f6ca183bb1837ac4520b283d3 | [
"arxiv",
"semantic_scholar"
] | SEAL: Searching Expandable Architectures for Incremental Learning | Incremental learning is a machine learning paradigm where a model learns from a sequential stream of tasks. This setting poses a key challenge: balancing plasticity (learning new tasks) and stability (preserving past knowledge). Neural Architecture Search (NAS), a branch of AutoML, automates the design of the architect... | [
"Matteo Gambella",
"Manuel Roveri"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-05-15T00:00:00 | https://arxiv.org/abs/2505.10457 | https://arxiv.org/pdf/2505.10457v2 | 2505.10457 | 10.48550/arXiv.2505.10457 | 1 | 0 | false | null | arXiv.org | 0.1604 |
3054ea9de00ecee5ff6559d64b3c9e3fbdbdcba3bcfc349412875a702c6015a0 | [
"arxiv",
"semantic_scholar"
] | PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models | Existing biomedical benchmarks do not provide end-to-end infrastructure for training, evaluation, and inference of models that integrate multimodal biological data and a broad range of machine learning tasks in therapeutics. We present PyTDC, an open-source machine-learning platform providing streamlined training, eval... | [
"Alejandro Velez-Arce",
"Jesus Caraballo",
"Marinka Zitnik"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-08T00:00:00 | https://arxiv.org/abs/2505.05577 | https://arxiv.org/pdf/2505.05577v2 | 2505.05577 | 10.48550/arXiv.2505.05577 | 1 | 0 | true | null | International Conference on Machine Learning | 0.2355 |
c4aa88ef6a33e714bfd5a3b0acb93472e90c27a30028ad12c11fd7527f1882b5 | [
"arxiv",
"semantic_scholar"
] | ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning | Effective and efficient graph representation learning is essential for enabling critical downstream tasks, such as node classification, link prediction, and subgraph search. However, existing graph neural network (GNN) architectures often struggle to adapt to diverse and complex graph structures, limiting their ability... | [
"Sixuan Wang",
"Jiao Yin",
"Jinli Cao",
"MingJian Tang",
"Hua Wang",
"Yanchun Zhang"
] | [
"cs.LG",
"cs.NE"
] | [
"Computer Science"
] | 2025-04-30T00:00:00 | https://arxiv.org/abs/2504.21254 | https://arxiv.org/pdf/2504.21254v3 | 2504.21254 | 10.1016/j.knosys.2025.114235 | 4 | 0 | true | https://github.com/sserranw/ABG-NAS | Knowledge-Based Systems | 0.2214 |
5991079d5be6bd31697963c547def40a673fa744e4ba2d7584db8c58c73bc15d | [
"arxiv",
"semantic_scholar"
] | Federated Neural Architecture Search with Model-Agnostic Meta Learning | Federated Learning (FL) often struggles with data heterogeneity due to the naturally uneven distribution of user data across devices. Federated Neural Architecture Search (NAS) enables collaborative search for optimal model architectures tailored to heterogeneous data to achieve higher accuracy. However, this process i... | [
"Xinyuan Huang",
"Jiechao Gao"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2025-04-08T00:00:00 | https://arxiv.org/abs/2504.06457 | https://arxiv.org/pdf/2504.06457v1 | 2504.06457 | 10.1109/BigData66926.2025.11400845 | 1 | 0 | false | null | BigData Congress [Services Society] | 0.118 |
1ce05f2c8534818db3254c4896a07a8bb4570fd5558e441a4ae88b881fb6c482 | [
"arxiv",
"semantic_scholar"
] | AutoML Benchmark with shorter time constraints and early stopping | Automated Machine Learning (AutoML) automatically builds machine learning (ML) models on data. The de facto standard for evaluating new AutoML frameworks for tabular data is the AutoML Benchmark (AMLB). AMLB proposed to evaluate AutoML frameworks using 1- and 4-hour time budgets across 104 tasks. We argue that shorter ... | [
"Israel Campero Jurado",
"Pieter Gijsbers",
"Joaquin Vanschoren"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-04-01T00:00:00 | https://arxiv.org/abs/2504.01222 | https://arxiv.org/pdf/2504.01222v3 | 2504.01222 | 10.48550/arXiv.2504.01222 | 1 | 0 | false | null | arXiv.org | 0.11 |
f2615ad77fdaa72516a6991aa838d8f40d9f211f9e41bdda8abbde698ede9bc4 | [
"arxiv",
"semantic_scholar"
] | RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection | Neural Architecture Search (NAS) is an automated technique to design optimal neural network architectures for a specific workload. Conventionally, evaluating candidate networks in NAS involves extensive training, which requires significant time and computational resources. To address this, training-free NAS has been pr... | [
"Tomomasa Yamasaki",
"Zhehui Wang",
"Tao Luo",
"Niangjun Chen",
"Bo Wang"
] | [
"cs.LG"
] | [
"Medicine",
"Computer Science"
] | 2025-03-26T00:00:00 | https://arxiv.org/abs/2503.22733 | https://arxiv.org/pdf/2503.22733v3 | 2503.22733 | 10.1109/TNNLS.2025.3552693 | 8 | 1 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.2386 |
ba3052f4a365e3aa9ca3e8856589cbe90762e16ca49deaf83cd5bcf24289cf42 | [
"arxiv",
"semantic_scholar"
] | Architecture-Aware Minimization (A$^2$M): How to Find Flat Minima in Neural Architecture Search | Neural Architecture Search (NAS) has become an essential tool for designing effective and efficient neural networks. In this paper, we investigate the geometric properties of neural architecture spaces commonly used in differentiable NAS methods, specifically NAS-Bench-201 and DARTS. By defining flatness metrics such a... | [
"Matteo Gambella",
"Fabrizio Pittorino",
"Manuel Roveri"
] | [
"cs.LG",
"cond-mat.dis-nn",
"cs.CV"
] | [
"Computer Science",
"Physics"
] | 2025-03-13T00:00:00 | https://arxiv.org/abs/2503.10404 | https://arxiv.org/pdf/2503.10404v3 | 2503.10404 | 10.1088/2632-2153/adf02e | 1 | 0 | true | https://github.com/AI-Tech-Research-Lab/AsquaredM | 2025 Mach. Learn.: Sci. Technol. 6 035016 | 0.1364 |
4c037600884632076f47305df791ff299a9940eb335895e6610f4086cb17ac86 | [
"arxiv",
"semantic_scholar"
] | Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning | A common belief is that intrinsically interpretable deep learning models ensure a correct, intuitive understanding of their behavior and offer greater robustness against accidental errors or intentional manipulation. However, these beliefs have not been comprehensively verified, and growing evidence casts doubt on them... | [
"Hubert Baniecki",
"Przemyslaw Biecek"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2025-03-11T00:00:00 | https://arxiv.org/abs/2503.08636 | https://arxiv.org/pdf/2503.08636v2 | 2503.08636 | 10.1007/s10994-025-06896-w | 4 | 0 | false | null | Machine-mediated learning | 0.1747 |
84462b390818557599e0c64177313b6f66ad65e70283be337bf979751ebf43fb | [
"arxiv",
"semantic_scholar"
] | A Perspective on Symbolic Machine Learning in Physical Sciences | Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learni... | [
"Nour Makke",
"Sanjay Chawla"
] | [
"cs.LG",
"hep-ph",
"hep-th"
] | [
"Computer Science",
"Physics"
] | 2025-02-25T00:00:00 | https://arxiv.org/abs/2502.17993 | https://arxiv.org/pdf/2502.17993v1 | 2502.17993 | 10.48550/arXiv.2502.17993 | 4 | 0 | false | null | arXiv.org | 0.1747 |
66bf16821e591c38a210ac56243bd21bc4acdb414103558c1c21e8095660bbaf | [
"arxiv",
"semantic_scholar"
] | Machine Learning-Based Cloud Computing Compliance Process Automation | Cloud computing adoption across industries has revolutionized enterprise operations while introducing significant challenges in compliance management. Organizations must continuously meet evolving regulatory requirements such as GDPR and ISO 27001, yet traditional manual review processes have become increasingly inadeq... | [
"Yuqing Wang",
"Xiao Yang"
] | [
"cs.LG",
"cs.AI",
"cs.CY",
"cs.DC"
] | [
"Computer Science"
] | 2025-02-22T00:00:00 | https://arxiv.org/abs/2502.16344 | https://arxiv.org/pdf/2502.16344v1 | 2502.16344 | 10.23977/autml.2025.060105 | 13 | 2 | false | null | Automated Machine Learning | 0.2865 |
6ed889b897cbd22fb4072d82f0b2497b52a0b802e8fa8df4cc21078a8ed0a331 | [
"arxiv",
"semantic_scholar"
] | Geometric Machine Learning on EEG Signals | Brain-computer interfaces (BCIs) offer transformative potential, but decoding neural signals presents significant challenges. The core premise of this paper is built around demonstrating methods to elucidate the underlying low-dimensional geometric structure present in high-dimensional brainwave data in order to assist... | [
"Benjamin J. Choi"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-02-07T00:00:00 | https://arxiv.org/abs/2502.05334 | https://arxiv.org/pdf/2502.05334v2 | 2502.05334 | 10.48550/arXiv.2502.05334 | 4 | 1 | false | null | arXiv.org | 0.1747 |
fba79a3a78c214d7b9961510c4b0acb386eddd122b7499bebb21ac21245915a1 | [
"arxiv",
"semantic_scholar"
] | Predicting concentration levels of air pollutants by transfer learning and recurrent neural network | Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been... | [
"Iat Hang Fong",
"Tengyue Li",
"Simon Fong",
"Raymond K. Wong",
"Antonio J. TallΓ³n-Ballesteros"
] | [
"cs.LG",
"cs.NE",
"physics.ao-ph"
] | [
"Computer Science",
"Physics"
] | 2025-01-30T00:00:00 | https://arxiv.org/abs/2502.01654 | https://arxiv.org/pdf/2502.01654v1 | 2502.01654 | 10.1016/j.knosys.2020.105622 | 85 | 3 | false | null | Knowledge-Based Systems | 0.4836 |
6ae19ac747da297caf8b5c7c3c1bed651226e25e3f65c6b747d7c749c7b6cfe5 | [
"arxiv",
"semantic_scholar"
] | U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection | Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitask approaches, there is minimal work investigating the impact of multitask learning on performance and fairness in depression detection nor l... | [
"Jiaee Cheong",
"Aditya Bangar",
"Sinan Kalkan",
"Hatice Gunes"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-01-16T00:00:00 | https://arxiv.org/abs/2501.09687 | https://arxiv.org/pdf/2501.09687v1 | 2501.09687 | 10.48550/arXiv.2501.09687 | 17 | 1 | false | null | Proceedings of Machine Learning Research 2024 | 0.3138 |
1e6951d5b45aeee6cb999160697978af16d7ae333562c918c21a34c90afa823f | [
"arxiv",
"semantic_scholar"
] | Effective Non-Random Extreme Learning Machine | The Extreme Learning Machine (ELM) is a growing statistical technique widely applied to regression problems. In essence, ELMs are single-layer neural networks where the hidden layer weights are randomly sampled from a specific distribution, while the output layer weights are learned from the data. Two of the key challe... | [
"Daniela De Canditiis",
"Fabiano Veglianti"
] | [
"stat.ML",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2024-11-25T00:00:00 | https://arxiv.org/abs/2411.16229 | https://arxiv.org/pdf/2411.16229v2 | 2411.16229 | 10.1007/s00521-025-11519-5 | 0 | 0 | false | null | Neural Computing and Applications (online 29 July 2025) | 0 |
2f71eb8b1aa1d366844209ad397adb7356c9a1f38dc7070c23b486095df884ee | [
"arxiv",
"semantic_scholar"
] | Delta-NAS: Difference of Architecture Encoding for Predictor-based Evolutionary Neural Architecture Search | Neural Architecture Search (NAS) continues to serve a key roll in the design and development of neural networks for task specific deployment. Modern NAS techniques struggle to deal with ever increasing search space complexity and compute cost constraints. Existing approaches can be categorized into two buckets: fine-gr... | [
"Arjun Sridhar",
"Yiran Chen"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-11-21T00:00:00 | https://arxiv.org/abs/2411.14498 | https://arxiv.org/pdf/2411.14498v1 | 2411.14498 | 10.1109/WACV61041.2025.00763 | 1 | 0 | false | null | IEEE Workshop/Winter Conference on Applications of Computer Vision | 0.0753 |
939cbe03e5a95b6161ffeb844db6cffd673cce63d1cf6b96d4a311b49e7da53b | [
"arxiv",
"semantic_scholar"
] | Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Models | Physics Informed Machine Learning has emerged as a popular approach for modeling and simulation in digital twins, enabling the generation of accurate models of processes and behaviors in real-world systems. However, existing methods either rely on simple loss regularizations that offer limited physics integration or em... | [
"Muhammad Saad Zia",
"Ashiq Anjum",
"Lu Liu",
"Anthony Conway",
"Anasol Pena Rios"
] | [
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.11497 | https://arxiv.org/pdf/2411.11497v2 | 2411.11497 | 10.1007/s10994-025-06808-y | 6 | 1 | false | null | Machine-mediated learning | 0.2113 |
59a459eea24d40bfae9744255895eca21e22b6eb99a024440c694a71d2579c21 | [
"arxiv",
"semantic_scholar"
] | Integrated Machine Learning and Survival Analysis Modeling for Enhanced Chronic Kidney Disease Risk Stratification | Chronic kidney disease (CKD) is a significant public health challenge, often progressing to end-stage renal disease (ESRD) if not detected and managed early. Early intervention, warranted by silent disease progression, can significantly reduce associated morbidity, mortality, and financial burden. In this study, we pro... | [
"Zachary Dana",
"Ahmed Ammar Naseer",
"Botros Toro",
"Sumanth Swaminathan"
] | [
"cs.LG",
"cs.AI",
"stat.CO",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-11-16T00:00:00 | https://arxiv.org/abs/2411.10754 | https://arxiv.org/pdf/2411.10754v1 | 2411.10754 | 10.48550/arXiv.2411.10754 | 4 | 0 | false | null | arXiv.org | 0.1747 |
bd0f104adc8c9adbd9860df9cffc3ad025ee353b185090b3b792116a2c97f8fc | [
"arxiv",
"semantic_scholar"
] | Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings | General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are l... | [
"DaphnΓ© Chopard",
"Sonia Laguna",
"Kieran Chin-Cheong",
"Annika Dietz",
"Anna Badura",
"Sven Wellmann",
"Julia E. Vogt"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2024-11-14T00:00:00 | https://arxiv.org/abs/2411.09821 | https://arxiv.org/pdf/2411.09821v4 | 2411.09821 | null | 0 | 0 | false | null | Machine Learning in Health Care | 0 |
7b4f5e84044291037cf3a04945b12d82f1f136f66969019988b7dc5f003c9a18 | [
"arxiv",
"semantic_scholar"
] | Hyperparameter Optimization in Machine Learning | Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these technologies. Manual hyperparameter search is often time-consumin... | [
"Luca Franceschi",
"Michele Donini",
"Valerio Perrone",
"Aaron Klein",
"CΓ©dric Archambeau",
"Matthias Seeger",
"Massimiliano Pontil",
"Paolo Frasconi"
] | [
"stat.ML",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2024-10-30T00:00:00 | https://arxiv.org/abs/2410.22854 | https://arxiv.org/pdf/2410.22854v3 | 2410.22854 | 10.1561/2200000088 | 9 | 0 | false | null | Foundations and Trends in Machine Learning, Vol. 18, No. 6 (2025) 1054-1201 | 0.25 |
52ad4e434a517294fe71e828e65bfb28d5226553ddfdd3154819f2e6cebea43f | [
"arxiv",
"semantic_scholar"
] | Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning | We study offline off-dynamics reinforcement learning (RL) to utilize data from an easily accessible source domain to enhance policy learning in a target domain with limited data. Our approach centers on return-conditioned supervised learning (RCSL), particularly focusing on Decision Transformer (DT) type frameworks, wh... | [
"Ruhan Wang",
"Yu Yang",
"Zhishuai Liu",
"Dongruo Zhou",
"Pan Xu"
] | [
"cs.LG",
"cs.AI",
"cs.RO",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-30T00:00:00 | https://arxiv.org/abs/2410.23450 | https://arxiv.org/pdf/2410.23450v2 | 2410.23450 | 10.48550/arXiv.2410.23450 | 14 | 0 | false | null | Transactions on Machine Learning Research, 2026 | 0.294 |
d244f818afce974bf87dbe7d9fffb5c86a95b0950adbd27675118e23aea1a544 | [
"arxiv",
"semantic_scholar"
] | The Effects of Multi-Task Learning on ReLU Neural Network Functions | This paper studies the properties of solutions to multi-task shallow ReLU neural network learning problems, wherein the network is trained to fit a dataset with minimal sum of squared weights. Remarkably, the solutions learned for each individual task resemble those obtained by solving a kernel regression problem, reve... | [
"Julia Nakhleh",
"Joseph Shenouda",
"Robert D. Nowak"
] | [
"stat.ML",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-29T00:00:00 | https://arxiv.org/abs/2410.21696 | https://arxiv.org/pdf/2410.21696v4 | 2410.21696 | 10.48550/arXiv.2410.21696 | 1 | 0 | false | null | arXiv.org | 0.0753 |
ae347ea10b5cacbe3963c8cf22a8842eb730f994427896e3abf6bf8985002103 | [
"arxiv",
"semantic_scholar"
] | SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning | Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks that autonomously build pipelines. While LLM-based agents have shown promise in automating machine learning tasks, they often generate low-... | [
"Yizhou Chi",
"Yizhang Lin",
"Sirui Hong",
"Duyi Pan",
"Yaying Fei",
"Guanghao Mei",
"Bangbang Liu",
"Tianqi Pang",
"Jacky Kwok",
"Ceyao Zhang",
"Bang Liu",
"Chenglin Wu"
] | [
"cs.AI",
"cs.CL",
"cs.LG",
"cs.SE"
] | [
"Computer Science"
] | 2024-10-22T00:00:00 | https://arxiv.org/abs/2410.17238 | https://arxiv.org/pdf/2410.17238v1 | 2410.17238 | 10.48550/arXiv.2410.17238 | 20 | 2 | true | https://github.com/geekan/MetaGPT | arXiv.org | 0.3306 |
bc61c1f41966e567090d401c9f3977ff28f619b7e2fe9652e2caeca5f9d18be5 | [
"arxiv",
"semantic_scholar"
] | The State of Julia for Scientific Machine Learning | Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's inception in 2012 and declaration of language goals in 2017, its ecosystem and language-level features have grown tremendously. In this paper... | [
"Edward Berman",
"Jacob Ginesin"
] | [
"cs.LG",
"cs.MS",
"cs.PL"
] | [
"Computer Science"
] | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.10908 | https://arxiv.org/pdf/2410.10908v2 | 2410.10908 | 10.48550/arXiv.2410.10908 | 2 | 0 | false | null | arXiv.org | 0.1193 |
52ae1ac296396790d9a359f7a298939acb019e0277e96e89552837b57e7e20fa | [
"arxiv",
"semantic_scholar"
] | Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques | A comprehensive guide to Automated Machine Learning (AutoML) is presented, covering fundamental principles, practical implementations, and future trends. The paper is structured to assist both beginners and experienced practitioners, with detailed discussions on popular AutoML tools such as TPOT, AutoGluon, and Auto-Ke... | [
"Pohsun Feng",
"Ziqian Bi",
"Yizhu Wen",
"Benji Peng",
"Junyu Liu",
"Caitlyn Heqi Yin",
"Tianyang Wang",
"Keyu Chen",
"Sen Zhang",
"Ming Li",
"Jiawei Xu",
"Ming Liu",
"Xuanhe Pan",
"Jinlang Wang",
"Xinyuan Song",
"Qian Niu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-10-12T00:00:00 | https://arxiv.org/abs/2410.09596 | https://arxiv.org/pdf/2410.09596v3 | 2410.09596 | 10.48550/arXiv.2410.09596 | 1 | 0 | false | null | arXiv.org | 0.0753 |
79659c2c098850941dda1357e7297579a3dd8988bcc48a7df745726018b67441 | [
"arxiv",
"semantic_scholar"
] | Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning | We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) (Pervez et al., 2024), we reduce the computational time and space complexit... | [
"Jiale Chen",
"Dingling Yao",
"Adeel Pervez",
"Dan Alistarh",
"Francesco Locatello"
] | [
"cs.LG",
"math.NA"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-08T00:00:00 | https://arxiv.org/abs/2410.06074 | https://arxiv.org/pdf/2410.06074v3 | 2410.06074 | 10.48550/arXiv.2410.06074 | 4 | 0 | true | https://github.com/IST-DASLab/ScalableMNN | International Conference on Learning Representations | 0.1747 |
3c9d92cf9cec5971a171c1e92ef7380140c7edf4d03d8367bdcec474896e0dca | [
"arxiv",
"semantic_scholar"
] | AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML | Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up complex tools, which is in general time-consuming and requires a large amount of hum... | [
"Patara Trirat",
"Wonyong Jeong",
"Sung Ju Hwang"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.MA"
] | [
"Computer Science"
] | 2024-10-03T00:00:00 | https://arxiv.org/abs/2410.02958 | https://arxiv.org/pdf/2410.02958v2 | 2410.02958 | 10.48550/arXiv.2410.02958 | 83 | 6 | false | null | International Conference on Machine Learning | 0.4811 |
d9d5d8e06a67bcff1fb144b808f54b7072015cde4a09fc0519b01d85d0c89084 | [
"arxiv",
"semantic_scholar"
] | Scalable Reinforcement Learning-based Neural Architecture Search | In this publication, we assess the ability of a novel Reinforcement Learning-based solution to the problem of Neural Architecture Search, where a Reinforcement Learning (RL) agent learns to search for good architectures, rather than to return a single optimal architecture. We consider both the NAS-Bench-101 and NAS- Be... | [
"Amber Cassimon",
"Siegfried Mercelis",
"Kevin Mets"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-10-02T00:00:00 | https://arxiv.org/abs/2410.01431 | https://arxiv.org/pdf/2410.01431v1 | 2410.01431 | 10.1007/s00521-024-10445-2 | 16 | 0 | false | null | null | 0.3076 |
7596743ec4668c9e8f7ebafe572cc02d92d879407d4e06cd7387d923ca4165cd | [
"arxiv",
"semantic_scholar"
] | A Survey on Neural Architecture Search Based on Reinforcement Learning | The automation of feature extraction of machine learning has been successfully realized by the explosive development of deep learning. However, the structures and hyperparameters of deep neural network architectures also make huge difference on the performance in different tasks. The process of exploring optimal struct... | [
"Wenzhu Shao"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-09-26T00:00:00 | https://arxiv.org/abs/2409.18163 | https://arxiv.org/pdf/2409.18163v2 | 2409.18163 | 10.48550/arXiv.2409.18163 | 1 | 0 | false | null | arXiv.org | 0.0753 |
dace4062c4689d284f7ae225ac4c13ff9b03051ddcb3395d00e5ef7db97eb799 | [
"arxiv",
"semantic_scholar"
] | Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification | We investigate the potential of using gravitational wave (GW) signals from rotating core-collapse supernovae to probe the equation of state (EOS) of nuclear matter. By generating GW signals from simulations with various EOSs, we train machine learning models to classify them and evaluate their performance. Our study bu... | [
"Y. Sultan Abylkairov",
"Matthew C. Edwards",
"Daniil Orel",
"Ayan Mitra",
"Bekdaulet Shukirgaliyev",
"Ernazar Abdikamalov"
] | [
"astro-ph.HE",
"gr-qc"
] | [
"Computer Science",
"Physics"
] | 2024-09-22T00:00:00 | https://arxiv.org/abs/2409.14508 | https://arxiv.org/pdf/2409.14508v2 | 2409.14508 | 10.1088/2632-2153/ada33a | 9 | 0 | false | null | Machine Learning: Science and Technology, 5(4), 045077 (2024) | 0.25 |
da0f40b8da30cd5273f96882825e1f7f16f931438336ed287bdfa1441e445990 | [
"arxiv",
"semantic_scholar"
] | Leveraging Machine Learning for Official Statistics: A Statistical Manifesto | It is important for official statistics production to apply ML with statistical rigor, as it presents both opportunities and challenges. Although machine learning has enjoyed rapid technological advances in recent years, its application does not possess the methodological robustness necessary to produce high quality st... | [
"Marco Puts",
"David Salgado",
"Piet Daas"
] | [
"stat.ML",
"cs.LG",
"stat.ME"
] | [
"Computer Science",
"Mathematics"
] | 2024-09-06T00:00:00 | https://arxiv.org/abs/2409.04365 | https://arxiv.org/pdf/2409.04365v1 | 2409.04365 | 10.48550/arXiv.2409.04365 | 3 | 0 | false | null | arXiv.org | 0.1505 |
ac796bc6bf943b572af021adadb3d7c3908e077d21bb6aff08992229a728c724 | [
"arxiv",
"semantic_scholar"
] | Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection | The rapid evolution of mobile networks from 5G to 6G has necessitated the development of autonomous network management systems, such as Zero-Touch Networks (ZTNs). However, the increased complexity and automation of these networks have also escalated cybersecurity risks. Existing Intrusion Detection Systems (IDSs) leve... | [
"Li Yang",
"Abdallah Shami"
] | [
"cs.LG",
"cs.CR",
"cs.NI"
] | [
"Computer Science"
] | 2024-09-05T00:00:00 | https://arxiv.org/abs/2409.03141 | https://arxiv.org/pdf/2409.03141v1 | 2409.03141 | 10.1145/3689933.3690833 | 18 | 0 | true | https://github.com/Western-OC2-Lab/AutonomousCyber-AutoML-based-Autonomous-Intrusion-Detection-System | null | 0.3197 |
506442f16cffd292ecfdeb0d4dc9b366b9aaaf1e8a392ab00e1cd96426f18320 | [
"arxiv",
"semantic_scholar"
] | Point Neuron Learning: A New Physics-Informed Neural Network Architecture | Machine learning and neural networks have advanced numerous research domains, but challenges such as large training data requirements and inconsistent model performance hinder their application in certain scientific problems. To overcome these challenges, researchers have investigated integrating physics principles int... | [
"Hanwen Bi",
"Thushara D. Abhayapala"
] | [
"cs.LG",
"cs.SD",
"eess.AS",
"eess.SP"
] | [
"Computer Science",
"Engineering"
] | 2024-08-30T00:00:00 | https://arxiv.org/abs/2408.16969 | https://arxiv.org/pdf/2408.16969v2 | 2408.16969 | 10.1186/s13636-024-00376-0 | 17 | 2 | false | null | EURASIP Journal on Audio, Speech, and Music Processing | 0.3138 |
2db0cb02b78d0876af803d3156665066638590795657a39a126a8b6eb88dd2db | [
"arxiv",
"semantic_scholar"
] | NAS-BNN: Neural Architecture Search for Binary Neural Networks | Binary Neural Networks (BNNs) have gained extensive attention for their superior inferencing efficiency and compression ratio compared to traditional full-precision networks. However, due to the unique characteristics of BNNs, designing a powerful binary architecture is challenging and often requires significant manpow... | [
"Zhihao Lin",
"Yongtao Wang",
"Jinhe Zhang",
"Xiaojie Chu",
"Haibin Ling"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-08-28T00:00:00 | https://arxiv.org/abs/2408.15484 | https://arxiv.org/pdf/2408.15484v1 | 2408.15484 | 10.48550/arXiv.2408.15484 | 10 | 0 | true | https://github.com/VDIGPKU/NAS-BNN | Pattern Recognition | 0.2603 |
91e88fa84e5c2e987fdb863deac9b922c9c705a4418af0c57d4998247a3d8a3b | [
"arxiv",
"semantic_scholar"
] | NAS-Cap: Deep-Learning Driven 3-D Capacitance Extraction with Neural Architecture Search and Data Augmentation | More accurate capacitance extraction is demanded for designing integrated circuits under advanced process technology. The pattern matching approach and the field solver for capacitance extraction have the drawbacks of inaccuracy and large computational cost, respectively. Recent work \cite{yang2023cnn} proposes a grid-... | [
"Haoyuan Li",
"Dingcheng Yang",
"Chunyan Pei",
"Wenjian Yu"
] | [
"cs.AR",
"cs.LG"
] | [
"Computer Science"
] | 2024-08-23T00:00:00 | https://arxiv.org/abs/2408.13195 | https://arxiv.org/pdf/2408.13195v1 | 2408.13195 | 10.48550/arXiv.2408.13195 | 0 | 0 | false | null | arXiv.org | 0 |
69d03fbf3717b245daeca52d237d96b9999deec232cc8092b3ae9fce18a14ec9 | [
"arxiv",
"semantic_scholar"
] | Machine Learning with Physics Knowledge for Prediction: A Survey | This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial pr... | [
"Joe Watson",
"Chen Song",
"Oliver Weeger",
"Theo Gruner",
"An T. Le",
"Kay Pompetzki",
"Ahmed Hendawy",
"Oleg Arenz",
"Will Trojak",
"Miles Cranmer",
"Carlo D'Eramo",
"Fabian BΓΌlow",
"Tanmay Goyal",
"Jan Peters",
"Martin W. Hoffman"
] | [
"cs.LG",
"math.NA",
"physics.comp-ph"
] | [
"Computer Science",
"Mathematics",
"Physics"
] | 2024-08-19T00:00:00 | https://arxiv.org/abs/2408.09840 | https://arxiv.org/pdf/2408.09840v2 | 2408.09840 | 10.48550/arXiv.2408.09840 | 12 | 0 | true | null | arXiv.org | 0.2785 |
09fea7021ce448340d35fa9d3323ee05a6d4c35d69b973d8b0767a3f0d0137ab | [
"arxiv",
"semantic_scholar"
] | Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost | Automated Machine Learning (AutoML) significantly simplifies the deployment of machine learning models by automating tasks from data preprocessing to model selection to ensembling. AutoML systems for tabular data often employ post hoc ensembling, where multiple models are combined to improve predictive accuracy. This t... | [
"Jannis Maier",
"Felix MΓΆller",
"Lennart Purucker"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-05T00:00:00 | https://arxiv.org/abs/2408.02280 | https://arxiv.org/pdf/2408.02280v1 | 2408.02280 | 10.48550/arXiv.2408.02280 | 0 | 0 | true | https://github.com/Atraxus/HA-ES | arXiv.org | 0 |
f3ab3f75e7da2c4ff47cebd65fc445a4a5b0efba8cfa7717c403d14f87e7631f | [
"arxiv",
"semantic_scholar"
] | Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction | Machine learning (ML) is revolutionising drug discovery by expediting the prediction of small molecule properties essential for developing new drugs. These properties -- including absorption, distribution, metabolism and excretion (ADME)-- are crucial in the early stages of drug development since they provide an unders... | [
"Alex G. C. de SΓ‘",
"David B. Ascher"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-01T00:00:00 | https://arxiv.org/abs/2408.00421 | https://arxiv.org/pdf/2408.00421v1 | 2408.00421 | 10.1145/3638530.3664166 | 2 | 0 | false | null | null | 0.1193 |
3fc422f5a84e1eb71003d7a059abb94ade0947daa220f3cc5acc1d475fedd49f | [
"arxiv",
"semantic_scholar"
] | Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning | Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefined budgets is crucial, with the goal of optimal allocations that maxim... | [
"Bobby Chen",
"Siyu Chen",
"Jason Dowlatabadi",
"Yu Xuan Hong",
"Vinayak Iyer",
"Uday Mantripragada",
"Rishabh Narang",
"Apoorv Pandey",
"Zijun Qin",
"Abrar Sheikh",
"Hongtao Sun",
"Jiaqi Sun",
"Matthew Walker",
"Kaichen Wei",
"Chen Xu",
"Jingnan Yang",
"Allen T. Zhang",
"Guoqing Z... | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-07-26T00:00:00 | https://arxiv.org/abs/2407.19078 | https://arxiv.org/pdf/2407.19078v1 | 2407.19078 | 10.48550/arXiv.2407.19078 | 2 | 0 | false | null | arXiv.org | 0.1193 |
c93bc5126c35c79615b01ffec9efb0dad9ef9f0d2514acffbcd4c510a552585e | [
"arxiv",
"semantic_scholar"
] | Confidence Interval Estimation of Predictive Performance in the Context of AutoML | Any supervised machine learning analysis is required to provide an estimate of the out-of-sample predictive performance. However, it is imperative to also provide a quantification of the uncertainty of this performance in the form of a confidence or credible interval (CI) and not just a point estimate. In an AutoML set... | [
"Konstantinos Paraschakis",
"Andrea Castellani",
"Giorgos Borboudakis",
"Ioannis Tsamardinos"
] | [
"cs.LG",
"cs.AI",
"cs.ET"
] | [
"Computer Science"
] | 2024-06-12T00:00:00 | https://arxiv.org/abs/2406.08099 | https://arxiv.org/pdf/2406.08099v1 | 2406.08099 | 10.48550/arXiv.2406.08099 | 1 | 0 | false | null | null | 0.0753 |
a37d6512c43498f6a950a777150edff34ec729491088d581cdfd93bb7d43413c | [
"arxiv",
"semantic_scholar"
] | Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation | Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each patient requires a unique stimulation policy to achieve optimal neural responses. Model-free reinforcement learning (MFR... | [
"Michelle Pan",
"Mariah Schrum",
"Vivek Myers",
"Erdem BΔ±yΔ±k",
"Anca Dragan"
] | [
"cs.LG",
"cs.AI",
"cs.HC"
] | [
"Computer Science"
] | 2024-06-10T00:00:00 | https://arxiv.org/abs/2406.06714 | https://arxiv.org/pdf/2406.06714v2 | 2406.06714 | 10.48550/arXiv.2406.06714 | 2 | 0 | false | null | International Conference on Machine Learning | 0.1193 |
9d57d56e4638f1c8767eb99fb490b7e15acda3917e2fee8c11ffad60fb140bdb | [
"arxiv",
"semantic_scholar"
] | Towards Neural Architecture Search for Transfer Learning in 6G Networks | The future 6G network is envisioned to be AI-native, and as such, ML models will be pervasive in support of optimizing performance, reducing energy consumption, and in coping with increasing complexity and heterogeneity. A key challenge is automating the process of finding optimal model architectures satisfying stringe... | [
"Adam Orucu",
"Farnaz Moradi",
"Masoumeh Ebrahimi",
"Andreas Johnsson"
] | [
"cs.NI",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-06-04T00:00:00 | https://arxiv.org/abs/2406.02333 | https://arxiv.org/pdf/2406.02333v1 | 2406.02333 | 10.48550/arXiv.2406.02333 | 3 | 0 | false | null | arXiv.org | 0.1505 |
a6d3b3dc9eb45ff27bf636c9cf2300dae59b610474f68a2720db789e37844a6b | [
"arxiv",
"semantic_scholar"
] | CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework | This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process. The proposed framework CE-NAS addresses the key challenge of high carbon cost associated with NAS by exploring the carbon emission variations of energy and energy differences of ... | [
"Yiyang Zhao",
"Yunzhuo Liu",
"Bo Jiang",
"Tian Guo"
] | [
"cs.LG",
"eess.SP"
] | [
"Computer Science",
"Engineering"
] | 2024-06-03T00:00:00 | https://arxiv.org/abs/2406.01414 | https://arxiv.org/pdf/2406.01414v2 | 2406.01414 | 10.48550/arXiv.2406.01414 | 8 | 0 | false | null | Neural Information Processing Systems | 0.2386 |
84267c07c87ae741ab15ee25e18f6fd5df345e15e24e4b63a3a61eddd97fc6ba | [
"arxiv",
"semantic_scholar"
] | Multi-Objective Neural Architecture Search by Learning Search Space Partitions | Deploying deep learning models requires taking into consideration neural network metrics such as model size, inference latency, and #FLOPs, aside from inference accuracy. This results in deep learning model designers leveraging multi-objective optimization to design effective deep neural networks in multiple criteria. ... | [
"Yiyang Zhao",
"Linnan Wang",
"Tian Guo"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-06-01T00:00:00 | https://arxiv.org/abs/2406.00291 | https://arxiv.org/pdf/2406.00291v2 | 2406.00291 | 10.48550/arXiv.2406.00291 | 4 | 0 | false | null | Journal of machine learning research | 0.1747 |
1d51ab6da6ab61eb3982cc0b1aa541aaddcd5d5a37380f3f643054f85d7c86b3 | [
"arxiv",
"semantic_scholar"
] | Is machine learning good or bad for the natural sciences? | Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standar... | [
"David W. Hogg",
"Soledad Villar"
] | [
"stat.ML",
"astro-ph.IM",
"cs.LG",
"physics.data-an"
] | [
"Computer Science",
"Mathematics",
"Physics"
] | 2024-05-28T00:00:00 | https://arxiv.org/abs/2405.18095 | https://arxiv.org/pdf/2405.18095v2 | 2405.18095 | 10.48550/arXiv.2405.18095 | 12 | 1 | false | null | International Conference on Machine Learning | 0.2785 |
e2d86a47b89466cef35bf3f6455030a504f80a777a4bee0e75297941493e0676 | [
"arxiv",
"semantic_scholar"
] | The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol | Neural Architecture Search (NAS) has been widely adopted to design neural networks for various computer vision tasks. One of its most promising subdomains is differentiable NAS (DNAS), where the optimal architecture is found in a differentiable manner. However, gradient-based methods suffer from the discretization erro... | [
"Konstanty Subbotko",
"Wojciech Jablonski",
"Piotr Bilinski"
] | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.NE"
] | [
"Computer Science"
] | 2024-05-26T00:00:00 | https://arxiv.org/abs/2405.16610 | https://arxiv.org/pdf/2405.16610v1 | 2405.16610 | 10.1109/CVPRW63382.2024.00173 | 1 | 0 | false | null | null | 0.0753 |
22afd240af960e85461b2ecbaaffbb5e73c85a13b924d3dad9b40e6a883b9a63 | [
"arxiv",
"semantic_scholar"
] | Detecting Moving Objects With Machine Learning | The scientific study of the Solar System's minor bodies ultimately starts with a search for those bodies. This chapter presents a review of the use of machine learning techniques to find moving objects, both natural and artificial, in astronomical imagery. After a short review of the classical non-machine learning tech... | [
"Wesley C. Fraser"
] | [
"astro-ph.EP",
"astro-ph.IM",
"cs.LG"
] | [
"Computer Science",
"Physics"
] | 2024-05-10T00:00:00 | https://arxiv.org/abs/2405.06148 | https://arxiv.org/pdf/2405.06148v1 | 2405.06148 | 10.48550/arXiv.2405.06148 | 2 | 0 | false | null | arXiv.org | 0.1193 |
38fd3d95e04b4585f9e931cff12221982555fa30d8de9bc201bbb0632457c81a | [
"arxiv",
"semantic_scholar"
] | AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models | AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM enables fine-tuning of foundation models with just three lines of code. Supporting various modalities including image, text, and tabular data, ... | [
"Zhiqiang Tang",
"Haoyang Fang",
"Su Zhou",
"Taojiannan Yang",
"Zihan Zhong",
"Tony Hu",
"Katrin Kirchhoff",
"George Karypis"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-04-24T00:00:00 | https://arxiv.org/abs/2404.16233 | https://arxiv.org/pdf/2404.16233v2 | 2404.16233 | 10.48550/arXiv.2404.16233 | 39 | 5 | true | null | null | 0.4005 |
bcbba22a0657d3fef89e668497eae1100088f43c98405f97f757270c925e5110 | [
"arxiv",
"semantic_scholar"
] | FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture Search | Neural Architecture Search (NAS) has emerged as a key tool in identifying optimal configurations of deep neural networks tailored to specific tasks. However, training and assessing numerous architectures introduces considerable computational overhead. One method to mitigating this is through performance predictors, whi... | [
"Haoming Zhang",
"Ran Cheng"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-04-24T00:00:00 | https://arxiv.org/abs/2404.15622 | https://arxiv.org/pdf/2404.15622v1 | 2404.15622 | 10.1109/IJCNN60899.2024.10651139 | 1 | 0 | true | https://github.com/EMI-Group/fr-nas | IEEE International Joint Conference on Neural Network | 0.0753 |
5d4f366e0e9d7d30476b803fe573c230d6732621716372b3c94df2c9133b3369 | [
"arxiv",
"semantic_scholar"
] | Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory | This research paper delves into the innovative integration of Shannon entropy and rough set theory, presenting a novel approach to generalize the evaluation approach in machine learning. The conventional application of entropy, primarily focused on information uncertainty, is extended through its combination with rough... | [
"Olga Cherednichenko",
"Dmytro Chernyshov",
"Dmytro Sytnikov",
"Polina Sytnikova"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-04-18T00:00:00 | https://arxiv.org/abs/2404.12511 | https://arxiv.org/pdf/2404.12511v1 | 2404.12511 | 10.48550/arXiv.2404.12511 | 1 | 0 | false | null | International Conference on Computational Linguistics and Intelligent Systems | 0.0753 |
718b3ca50bdef2301034ac7e19195a8baf59e39b870c3d69bd3846e16de8d0f7 | [
"arxiv",
"semantic_scholar"
] | TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search | Neural Architecture Search (NAS) is a powerful technique for discovering high-performing CNN architectures, but most existing methods rely on costly training or extensive sampling. Zero-shot NAS offers a training-free alternative by using proxies to predict architecture performance. However, existing proxies are often ... | [
"Ye Qiao",
"Jingcheng Li",
"Haocheng Xu",
"Sitao Huang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-03-30T00:00:00 | https://arxiv.org/abs/2404.00271 | https://arxiv.org/pdf/2404.00271v2 | 2404.00271 | null | 2 | 0 | false | null | null | 0.1193 |
42e838750bb03da6ff57df4a94175e4920c919f9e27c55b57826533cd11c9aac | [
"arxiv",
"semantic_scholar"
] | Multi-Objective Evolutionary Neural Architecture Search for Recurrent Neural Networks | Artificial neural network (NN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) serves to automate the design of NN architectures and has proven to be successful in automatically finding NN architectures that outperform tho... | [
"Reinhard Booysen",
"Anna Sergeevna Bosman"
] | [
"cs.NE",
"cs.LG"
] | [
"Computer Science"
] | 2024-03-17T00:00:00 | https://arxiv.org/abs/2403.11173 | https://arxiv.org/pdf/2403.11173v1 | 2403.11173 | 10.1007/s11063-024-11659-0 | 5 | 1 | false | null | Neural Processing Letters | 0.1945 |
6ab89ee110fb8d8cd99d275a45b5f2a7bbfdaedd4d781730ba0b07eada715924 | [
"arxiv",
"semantic_scholar"
] | Multi-conditioned Graph Diffusion for Neural Architecture Search | Neural architecture search automates the design of neural network architectures usually by exploring a large and thus complex architecture search space. To advance the architecture search, we present a graph diffusion-based NAS approach that uses discrete conditional graph diffusion processes to generate high-performin... | [
"Rohan Asthana",
"Joschua Conrad",
"Youssef Dawoud",
"Maurits Ortmanns",
"Vasileios Belagiannis"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2024-03-09T00:00:00 | https://arxiv.org/abs/2403.06020 | https://arxiv.org/pdf/2403.06020v2 | 2403.06020 | 10.48550/arXiv.2403.06020 | 17 | 3 | false | null | null | 0.3138 |
952d11d5190f325399e1c27f0d98bf85ba6116b5ed2832736e34aded86fb9afe | [
"arxiv",
"semantic_scholar"
] | FlatNAS: optimizing Flatness in Neural Architecture Search for Out-of-Distribution Robustness | Neural Architecture Search (NAS) paves the way for the automatic definition of Neural Network (NN) architectures, attracting increasing research attention and offering solutions in various scenarios. This study introduces a novel NAS solution, called Flat Neural Architecture Search (FlatNAS), which explores the interpl... | [
"Matteo Gambella",
"Fabrizio Pittorino",
"Manuel Roveri"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2024-02-29T00:00:00 | https://arxiv.org/abs/2402.19102 | https://arxiv.org/pdf/2402.19102v1 | 2402.19102 | 10.1109/IJCNN60899.2024.10650433 | 7 | 0 | false | null | IEEE International Joint Conference on Neural Network | 0.2258 |
1086132161c6638766dffaecd2ef7d8cee62050dac3e0145f959071dbed55568 | [
"arxiv",
"semantic_scholar"
] | Towards Assessing Spread in Sets of Software Architecture Designs | Several approaches have recently used automated techniques to generate architecture design alternatives by means of optimization techniques. These approaches aim at improving an initial architecture with respect to quality aspects, such as performance, reliability, or maintainability. In this context, each optimization... | [
"Vittorio Cortellessa",
"J. Andres Diaz-Pace",
"Daniele Di Pompeo",
"Michele Tucci"
] | [
"cs.SE",
"cs.PF"
] | [
"Computer Science"
] | 2024-02-29T00:00:00 | https://arxiv.org/abs/2402.19171 | https://arxiv.org/pdf/2402.19171v1 | 2402.19171 | 10.1007/978-3-031-42592-9_9 | 2 | 0 | false | null | European Conference on Software Architecture | 0.1193 |
324217c5f9dce9d735c48682d256f669d62a68115bc4c329e881cc85aeef2627 | [
"arxiv",
"semantic_scholar"
] | Evolving machine learning workflows through interactive AutoML | Automatic workflow composition (AWC) is a relevant problem in automated machine learning (AutoML) that allows finding suitable sequences of preprocessing and prediction models together with their optimal hyperparameters. This problem can be solved using evolutionary algorithms and, in particular, grammar-guided genetic... | [
"Rafael Barbudo",
"Aurora RamΓrez",
"JosΓ© RaΓΊl Romero"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-02-28T00:00:00 | https://arxiv.org/abs/2402.18505 | https://arxiv.org/pdf/2402.18505v1 | 2402.18505 | 10.48550/arXiv.2402.18505 | 2 | 1 | false | null | arXiv.org | 0.1505 |
acd2ef72af0ba5bc82b6331ad690d8702beff73c6f76572d5bf24ff934811d29 | [
"arxiv",
"semantic_scholar"
] | Automated Machine Learning for Multi-Label Classification | Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For supervised learning tasks, most notably binary and multinomial classification, aka single-label classification (SLC), such AutoML approaches ha... | [
"Marcel Wever"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-02-28T00:00:00 | https://arxiv.org/abs/2402.18198 | https://arxiv.org/pdf/2402.18198v1 | 2402.18198 | 10.17619/UNIPB/1-1302 | 1 | 0 | false | null | arXiv.org | 0.0753 |
f62ea0b30a2f481c816217b78f7eafe264393555b33886203e4d1a607836a4b9 | [
"arxiv",
"semantic_scholar"
] | A Quick Introduction to Quantum Machine Learning for Non-Practitioners | This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches. Quantum computing utilizes particles governed by quantum mechanics for computational purposes, leveraging pr... | [
"Ethan N. Evans",
"Dominic Byrne",
"Matthew G. Cook"
] | [
"quant-ph",
"cs.ET",
"cs.LG"
] | [
"Computer Science",
"Physics"
] | 2024-02-22T00:00:00 | https://arxiv.org/abs/2402.14694 | https://arxiv.org/pdf/2402.14694v1 | 2402.14694 | 10.48550/arXiv.2402.14694 | 5 | 0 | false | null | arXiv.org | 0.1945 |
f9b0b5dd1c9b048d35a5caca62d11424fb4f71bb5dacad720955d4412685fe43 | [
"arxiv",
"semantic_scholar"
] | Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning | The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. This work demonstrates the feasibility and advantages of applying explainable machine learning techniques to the analysis of quantum dot measurements, pav... | [
"Daniel Schug",
"Tyler J. Kovach",
"M. A. Wolfe",
"Jared Benson",
"Sanghyeok Park",
"J. P. Dodson",
"J. Corrigan",
"M. A. Eriksson",
"Justyna P. Zwolak"
] | [
"cs.CV",
"cond-mat.mes-hall",
"cs.LG"
] | [
"Computer Science",
"Physics"
] | 2024-02-21T00:00:00 | https://arxiv.org/abs/2402.13699 | https://arxiv.org/pdf/2402.13699v5 | 2402.13699 | 10.1088/2632-2153/ada087 | 7 | 0 | false | null | Mach. Learn.: Sci. Technol. 6, 015006 (2025) | 0.2258 |
End of preview. Expand in Data Studio
Neural Architecture Search (NAS) Papers β FineSet
A research-paper dataset on Neural Architecture Search (NAS) Papers, assembled, deduplicated, and quality-scored by FineSet from arXiv and Semantic Scholar.
πΈ This is a dated snapshot β generated 2026-06-19. It is not auto-updated. Research on Neural Architecture Search (NAS) Papers moves fast β new papers land on arXiv every week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. β
Why this dataset
- Quality-scored:
quality_scorefloat (0β1), blends citations with recency + code/venue signals β filter out the noise - Papers with code: 76 flagged via
has_codeβ find reproducible work fast - Deduplicated: arXiv + Semantic Scholar cross-referenced, duplicate records merged
- Clean JSONL: 427 records, one per line, normalized fields β no encoding garbage
Dataset details
- Records: 427
- Date range: 2019β2026
- Snapshot date: 2026-06-19 (frozen β see note above)
- Sources: arXiv, Semantic Scholar (cross-referenced, duplicates merged)
- arXiv categories: cs.LG, cs.NE
- Quality scoring: citations + recency + code/venue blend, 0β1 (p50=0.301, p90=0.594)
- Format: JSONL, one record per line
Fields
| Field | Type | Description |
|---|---|---|
| id | string | Deterministic SHA256 record id |
| sources | list | Which sources contributed (arxiv, semantic_scholar) |
| title | string | Paper title |
| abstract | string | Full abstract |
| authors | list | Author names |
| categories | list | arXiv category codes |
| fields_of_study | list | Semantic Scholar field tags |
| published_date | string | ISO 8601 date |
| url | string | arXiv abstract URL |
| pdf_url | string|null | Open-access PDF if available |
| arxiv_id | string|null | arXiv identifier |
| doi | string|null | DOI if available |
| citation_count | int | Citation count (Semantic Scholar) |
| influential_citation_count | int | Influential citations (Semantic Scholar) |
| has_code | bool | Code repo detected in the arXiv comment |
| code_url | string|null | GitHub URL if detected |
| venue | string|null | Publication venue |
| quality_score | float | 0β1, blended (citations + recency + code/venue) |
Quality score methodology
quality_score = max(impact, freshness), clamped to [0, 1], where:
- impact =
max( log10(citations+1)/4 , log10(influential_citations+1)/2 )β realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+). - freshness =
recency Γ (0.35 + 0.30Β·has_code + 0.20Β·has_venue)β a baseline for recent papers (so a strong paper published this week isn't scored 0 just for lacking citations), whererecencyis 1.0 for papers β€60 days old and decays linearly to 0 by ~18 months.
Old highly-cited papers score on impact; brand-new papers score on freshness; old uncited papers score ~0. Useful for filtering training data by quality, not just age.
π Want this on YOUR topic, updated daily?
This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this refreshed every day on whatever topic you describe β new papers in, dedup and quality scoring automatic, export as JSONL/Parquet or push straight to the Hub.
Tell me the topic you'd want and I'll run the pipeline on it β open a discussion on this dataset, it's free and it's how I decide what to build next.
β fineset.io β describe what you want to train on, get a dataset. Early-access waitlist open (referral skip available).
- Downloads last month
- 28