Neural Architecture Search

Also known as NAS

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Overview

Neural Architecture Search (NAS) studies how to choose or generate a neural network’s structure so it performs well on a task without relying entirely on manual design. Common approaches include differentiable search such as DARTS, evolutionary and reinforcement-learning methods, weight-sharing supernets, and surrogate or zero-cost predictors that estimate candidate quality cheaply. Increasingly, search also accounts for latency, memory, energy, quantization, and deployment conditions, using hardware measurements or learned cost models alongside accuracy. The central tension is how to make search economical while ensuring that cheap proxies and benchmark results reliably predict performance after full training and on real devices.

Latest papers

CardsList

132 papers21 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

  1. Auto Researching, not hyperparameter tuning: Convergence Analysis of 10,000 Experiments

    Mar 16, 2026Xiaoyi LiLarge Language Model-Guided NASAutonomous Experimentation

  2. PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units

    Mar 16, 2026Mark Deutel, Simon Geis, Axel PlingeNeural Architecture SearchHardware-Aware NAS

    Fraunhofer Institute for Integrated Circuits, Fraunhofer IIS, Germany

  3. Resource-Efficient Iterative LLM-Based NAS with Feedback Memory

    Mar 12, 2026Xiaojie Gu, Dmitry Ignatov, Radu TimofteLarge Language Model-Guided NASNeural Architecture Search

    Computer Vision Lab, CAIDAS & IFI, University of Würzburg, Germany

  4. YOLO-NAS-Bench: A Surrogate Benchmark with Self-Evolving Predictors for YOLO Architecture Search

    Mar 10, 2026Zhe Li, Xiaoyu Ding, Jiaxin Zheng +1Neural Architecture SearchObject Detection

    Wangxuan Institute of Computer Technology, Peking University

  5. AutoResearch-RL: Perpetual Self-Evaluating Reinforcement Learning Agents for Autonomous Neural Architecture Discovery

    Mar 7, 2026Nilesh Jain, Rohit Yadav, Sagar Kotian +1Autonomous Research AgentsAutonomous Experimentation

    Yale · Google Cloud · Stanford +5

  6. Search Multilayer Perceptron-Based Fusion for Efficient and Accurate Siamese Tracking

    Mar 2, 2026Tianqi Shen, Huakao Lin, Ning AnVisual Object TrackingMultilayer Perceptrons

    Institute of Mining Artificial Intelligence, Chinese Institute of Coal Science Department of Computer Science, City University of Hong Kong Beijing 100013, China / Hong Kong SAR · Image Processing Center, School of Astronautics, Beihang University Beijing 100191, China · Institute of Mining Artificial Intelligence, Chinese Institute of Coal Science State Key Laboratory of Intelligent Coal Mining and Strata Control Beijing 100013, China

  7. Optimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search

    Feb 25, 2026Liping Meng, Fan Nie, Yunyun Zhang +1Medical Image SegmentationNeural Architecture Search

  8. DGPO: RL-Steered Graph Diffusion for Neural Architecture Generation

    Feb 22, 2026Aleksei Liuliakov, Luca Hermes, Barbara HammerGraph GenerationGraph Diffusion

    Machine Learning Group, Bielefeld University, Germany

  9. SEval-NAS: A Search-Agnostic Evaluation for Neural Architecture Search

    Feb 17, 2026Atah Nuh Mih, Jianzhou Wang, Truong Thanh Hung Nguyen +1Neural Architecture Performance PredictionNeural Architecture Search

    Analytics Everywhere Lab, University of New Brunswick, Canada

  10. MIDAS: Mosaic Input-Specific Differentiable Architecture Search

    Feb 6, 2026Konstanty SubbotkoNeural Architecture SearchDynamic Neural Networks

    Independent Researcher

  11. Neural Architecture Discovery via Autonomous Evolution

    Jul 24, 2025Weixian Xu, Yixiu Liu, Yang Nan +4Autonomous Research AgentsAI Agents for Scientific Discovery

    Shanghai Jiao Tong University · SII · GAIR +1

  12. SEAL: Searching Expandable Architectures for Incremental Learning

    May 15, 2025Matteo Gambella, Manuel RoveriDynamic Neural NetworksContinual Learning

    Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy