{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/architecture-search/papers/14","list_of":"/task/architecture-search","task":"Neural Architecture Search","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":14,"pages_in_order":20,"rows_per_page":100,"rows":[1301,1400],"of":1915,"counts":{"archive_papers_tagged":1915,"with_a_code_link":859,"where_syntology_ran_a_sample":243,"not_listed_spam_title":0,"listed":1915,"listed_where_code_ran":243,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":204,"every_run_a_failure_of_syntologys_instrument":39,"listed_with_a_run_with_no_instrument_failure":204,"listed_every_run_a_failure_of_syntologys_instrument":39,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/architecture-search","prev":"/task/architecture-search/papers/13","next":"/task/architecture-search/papers/15","papers":[{"url":null,"slug":"warm-starting-darts-using-meta-learning","title":"Warm-starting DARTS using meta-learning","date":"2022-05-12","arxiv_id":"2205.06355","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-collaboration-strategy-in-the-mining-pool","title":"A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus","date":"2022-05-05","arxiv_id":"2206.07089","repositories_listed":0,"syntology":null},{"url":null,"slug":"unrealnas-can-we-search-neural-architectures","title":"UnrealNAS: Can We Search Neural Architectures with Unreal Data?","date":"2022-05-04","arxiv_id":"2205.02162","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-stochastic-bilevel-optimization-with","title":"Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction","date":"2022-05-03","arxiv_id":"2205.01608","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-initial-look-at-self-reprogramming","title":"Self-Programming Artificial Intelligence Using Code-Generating Language Models","date":"2022-04-30","arxiv_id":"2205.00167","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-neural-architecture-search-spaces","title":"Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering","date":"2022-04-29","arxiv_id":"2204.14103","repositories_listed":0,"syntology":null},{"url":null,"slug":"maple-edge-a-runtime-latency-predictor-for","title":"MAPLE-Edge: A Runtime Latency Predictor for Edge Devices","date":"2022-04-27","arxiv_id":"2204.12950","repositories_listed":0,"syntology":null},{"url":"/paper/pre-nas-predictor-assisted-evolutionary","slug":"pre-nas-predictor-assisted-evolutionary","title":"PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search","date":"2022-04-27","arxiv_id":"2204.12726","repositories_listed":0,"syntology":null},{"url":null,"slug":"enable-deep-learning-on-mobile-devices","title":"Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications","date":"2022-04-25","arxiv_id":"2204.11786","repositories_listed":0,"syntology":null},{"url":null,"slug":"pvnas-3d-neural-architecture-search-with","title":"PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution","date":"2022-04-25","arxiv_id":"2204.11797","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-privacy-preserving-neural","title":"Towards Privacy-Preserving Neural Architecture Search","date":"2022-04-22","arxiv_id":"2204.10958","repositories_listed":0,"syntology":null},{"url":null,"slug":"hasa-hybrid-architecture-search-with","title":"HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images","date":"2022-04-14","arxiv_id":"2204.06697","repositories_listed":0,"syntology":null},{"url":null,"slug":"de-ireps-searching-for-improved-re","title":"Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy","date":"2022-04-13","arxiv_id":"2204.06403","repositories_listed":0,"syntology":null},{"url":null,"slug":"splitnets-designing-neural-architectures-for","title":"SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems","date":"2022-04-10","arxiv_id":"2204.04705","repositories_listed":0,"syntology":null},{"url":null,"slug":"searching-for-efficient-neural-architectures","title":"Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs","date":"2022-04-09","arxiv_id":"2204.14007","repositories_listed":0,"syntology":null},{"url":null,"slug":"supernet-in-neural-architecture-search-a","title":"A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search","date":"2022-04-08","arxiv_id":"2204.03916","repositories_listed":0,"syntology":null},{"url":null,"slug":"shiftnas-towards-automatic-generation-of","title":"ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks","date":"2022-04-07","arxiv_id":"2204.05113","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architecture-search-for-speech-emotion","title":"Neural Architecture Search for Speech Emotion Recognition","date":"2022-03-31","arxiv_id":"2203.16928","repositories_listed":0,"syntology":null},{"url":null,"slug":"quasi-orthogonality-and-intrinsic-dimensions","title":"Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation","date":"2022-03-30","arxiv_id":"2203.16687","repositories_listed":0,"syntology":null},{"url":null,"slug":"autocomet-smart-neural-architecture-search","title":"AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement","date":"2022-03-29","arxiv_id":"2203.15408","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-and-energy-efficient-ppg-based-heart","title":"Robust and Energy-efficient PPG-based Heart-Rate Monitoring","date":"2022-03-28","arxiv_id":"2203.16339","repositories_listed":0,"syntology":null},{"url":null,"slug":"autots-automatic-time-series-forecasting","title":"AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning","date":"2022-03-26","arxiv_id":"2203.14169","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-intelligent-end-to-end-neural-architecture","title":"An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development","date":"2022-03-25","arxiv_id":"2203.13563","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotionnas-two-stream-architecture-search-for","title":"EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition","date":"2022-03-25","arxiv_id":"2203.13617","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-of-nas-for-few-shot-learning-in","title":"Meta-Learning of NAS for Few-shot Learning in Medical Image Applications","date":"2022-03-16","arxiv_id":"2203.08951","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-bi-directional-skip-connections-in","title":"Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond","date":"2022-03-11","arxiv_id":"2203.05709","repositories_listed":0,"syntology":null},{"url":null,"slug":"uenas-a-unified-evolution-based-nas-framework","title":"Multi-trial Neural Architecture Search with Lottery Tickets","date":"2022-03-08","arxiv_id":"2203.04300","repositories_listed":0,"syntology":null},{"url":null,"slug":"searching-for-robust-neural-architectures-via","title":"$A^{3}D$: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks","date":"2022-03-07","arxiv_id":"2203.03128","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-energy-efficiency-and","title":"Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images","date":"2022-03-04","arxiv_id":"2203.02571","repositories_listed":0,"syntology":null},{"url":null,"slug":"wpnas-neural-architecture-search-by-jointly","title":"WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor","date":"2022-03-04","arxiv_id":"2203.02086","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-neural-architecture-search-for","title":"Fast Neural Architecture Search for Lightweight Dense Prediction Networks","date":"2022-03-03","arxiv_id":"2203.01994","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-constrained-optimization-approach-to","title":"A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima","date":"2022-03-01","arxiv_id":"2203.01123","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-temporal-convolutional-networks-for","title":"Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring","date":"2022-03-01","arxiv_id":"2203.04396","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-nas-an-online-neuroevolution-based-neural","title":"ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting","date":"2022-02-27","arxiv_id":"2202.13471","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hardware-aware-system-for-accelerating-deep","title":"A Hardware-Aware System for Accelerating Deep Neural Network Optimization","date":"2022-02-25","arxiv_id":"2202.12954","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-neural-architecture-exploration","title":"Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms","date":"2022-02-25","arxiv_id":"2202.12934","repositories_listed":0,"syntology":null},{"url":null,"slug":"sutd-prcm-dataset-and-neural-architecture","title":"SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex Metasurface Design","date":"2022-02-24","arxiv_id":"2203.00002","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-block-neural-architecture-search-for","title":"Mixed-Block Neural Architecture Search for Medical Image Segmentation","date":"2022-02-23","arxiv_id":"2202.11401","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-larger-the-fairer-small-neural-networks","title":"The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices","date":"2022-02-23","arxiv_id":"2202.11317","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-tailored-models-on-private-aiot","title":"Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search","date":"2022-02-23","arxiv_id":"2202.11490","repositories_listed":0,"syntology":null},{"url":null,"slug":"deephybrid-deep-learning-on-automotive-radar","title":"DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification","date":"2022-02-17","arxiv_id":"2202.08519","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-architectural-fine-tuning-with","title":"Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification","date":"2022-02-17","arxiv_id":"2202.08604","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architecture-search-for-dense","title":"Neural Architecture Search for Dense Prediction Tasks in Computer Vision","date":"2022-02-15","arxiv_id":"2202.07242","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architecture-search-for-energy","title":"Neural Architecture Search for Energy Efficient Always-on Audio Models","date":"2022-02-09","arxiv_id":"2202.05397","repositories_listed":0,"syntology":null},{"url":null,"slug":"heed-the-noise-in-performance-evaluations-in","title":"Heed the Noise in Performance Evaluations in Neural Architecture Search","date":"2022-02-04","arxiv_id":"2202.02078","repositories_listed":0,"syntology":null},{"url":null,"slug":"autodistil-few-shot-task-agnostic-neural","title":"AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models","date":"2022-01-29","arxiv_id":"2201.12507","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-semi-supervised-neural-architecture","title":"Self Semi Supervised Neural Architecture Search for Semantic Segmentation","date":"2022-01-29","arxiv_id":"2201.12646","repositories_listed":0,"syntology":null},{"url":null,"slug":"framed-data-driven-structural-performance","title":"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames","date":"2022-01-25","arxiv_id":"2201.10459","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architecture-searching-for-facial","title":"Neural Architecture Searching for Facial Attributes-based Depression Recognition","date":"2022-01-24","arxiv_id":"2201.09799","repositories_listed":0,"syntology":null},{"url":null,"slug":"autodistill-an-end-to-end-framework-to","title":"AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models","date":"2022-01-21","arxiv_id":"2201.08539","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deeper-look-at-zero-cost-proxies-for","title":"A Deeper Look at Zero-Cost Proxies for Lightweight NAS","date":"2022-01-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"landscape-of-neural-architecture-search","title":"Landscape of Neural Architecture Search across sensors: how much do they differ ?","date":"2022-01-17","arxiv_id":"2201.06321","repositories_listed":0,"syntology":null},{"url":null,"slug":"udc-unified-dnas-for-compressible-tinyml","title":"UDC: Unified DNAS for Compressible TinyML Models","date":"2022-01-15","arxiv_id":"2201.05842","repositories_listed":0,"syntology":null},{"url":null,"slug":"winning-solutions-and-post-challenge-analyses","title":"Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019","date":"2022-01-11","arxiv_id":"2201.03801","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architecture-search-for-inversion","title":"Neural Architecture Search for Inversion","date":"2022-01-05","arxiv_id":"2201.01772","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-consistent-neural-architecture","title":"Distribution Consistent Neural Architecture Search","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-aware-mutual-knowledge","title":"Performance-Aware Mutual Knowledge Distillation for Improving Neural Architecture Search","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"searching-the-deployable-convolution-neural","title":"Searching the Deployable Convolution Neural Networks for GPUs","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-mixed-precision-quantization-search","title":"Automatic Mixed-Precision Quantization Search of BERT","date":"2021-12-30","arxiv_id":"2112.14938","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-layer-wise-connections-in-graph-neural","title":"Learn Layer-wise Connections in Graph Neural Networks","date":"2021-12-27","arxiv_id":"2112.13585","repositories_listed":0,"syntology":null},{"url":null,"slug":"spider-searching-personalized-neural","title":"SPIDER: Searching Personalized Neural Architecture for Federated Learning","date":"2021-12-27","arxiv_id":"2112.13939","repositories_listed":0,"syntology":null},{"url":null,"slug":"darts-without-a-validation-set-optimizing-the","title":"DARTS without a Validation Set: Optimizing the Marginal Likelihood","date":"2021-12-24","arxiv_id":"2112.13023","repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-nas-with-automated-super-network","title":"Enabling NAS with Automated Super-Network Generation","date":"2021-12-20","arxiv_id":"2112.10878","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypersegnas-bridging-one-shot-neural","title":"HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet","date":"2021-12-20","arxiv_id":"2112.10652","repositories_listed":0,"syntology":null},{"url":null,"slug":"m-fasterseg-an-efficient-semantic","title":"M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search","date":"2021-12-15","arxiv_id":"2112.07918","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-x3d-ultra-efficient-video-understanding","title":"Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search","date":"2021-12-09","arxiv_id":"2112.04710","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-exploration-in-neural-feature","title":"Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers","date":"2021-12-07","arxiv_id":"2112.03487","repositories_listed":0,"syntology":null},{"url":null,"slug":"rsbnet-one-shot-neural-architecture-search","title":"RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition","date":"2021-12-07","arxiv_id":"2112.03456","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-complicated-search-spaces-with-1","title":"Exploring Complicated Search Spaces with Interleaving-Free Sampling","date":"2021-12-05","arxiv_id":"2112.02488","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-free-neural-architecture-search-via","title":"Data-Free Neural Architecture Search via Recursive Label Calibration","date":"2021-12-03","arxiv_id":"2112.02086","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-differentiable-architecture-search-with","title":"Graph Differentiable Architecture Search with Structure Learning","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-mistakes-based-on-class","title":"Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search","date":"2021-12-01","arxiv_id":"2112.00275","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-efficient-patch-based-inference-for","title":"Memory-efficient Patch-based Inference for Tiny Deep Learning","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-feature-interaction-search-for","title":"Progressive Feature Interaction Search for Deep Sparse Network","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/tnasp-a-transformer-based-nas-predictor-with","slug":"tnasp-a-transformer-based-nas-predictor-with","title":"TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"training-batchnorm-only-in-neural","title":"Training BatchNorm Only in Neural Architecture Search and Beyond","date":"2021-12-01","arxiv_id":"2112.00265","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-differentiable-architecture-search","title":"Improving Differentiable Architecture Search with a Generative Model","date":"2021-11-30","arxiv_id":"2112.00171","repositories_listed":0,"syntology":null},{"url":null,"slug":"maple-microprocessor-a-priori-for-latency","title":"MAPLE: Microprocessor A Priori for Latency Estimation","date":"2021-11-30","arxiv_id":"2111.15106","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-precision-low-bit-quantization-of","title":"Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition","date":"2021-11-29","arxiv_id":"2112.11438","repositories_listed":0,"syntology":null},{"url":"/paper/balenas-differentiable-architecture-search","slug":"balenas-differentiable-architecture-search","title":"BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule","date":"2021-11-25","arxiv_id":"2111.13204","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-resistant-graph-neural-networks-via","title":"Anomaly-resistant Graph Neural Networks via Neural Architecture Search","date":"2021-11-22","arxiv_id":"2111.11406","repositories_listed":0,"syntology":null},{"url":"/paper/fbnetv5-neural-architecture-search-for","slug":"fbnetv5-neural-architecture-search-for","title":"FBNetV5: Neural Architecture Search for Multiple Tasks in One Run","date":"2021-11-19","arxiv_id":"2111.10007","repositories_listed":0,"syntology":null},{"url":null,"slug":"jmsnas-joint-model-split-and-neural","title":"JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks","date":"2021-11-16","arxiv_id":"2111.08206","repositories_listed":0,"syntology":null},{"url":null,"slug":"shrinknas-single-path-one-shot-operator","title":"ShrinkNAS : Single-Path One-Shot Operator Exploratory Training for Transformer with Dynamic Space Shrinking","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supershaper-task-agnostic-super-pre-training-1","title":"SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-criteria-approach-to-evolve-sparse","title":"A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting","date":"2021-11-15","arxiv_id":"2111.08060","repositories_listed":0,"syntology":null},{"url":null,"slug":"searching-for-trionet-combining-convolution","title":"Searching for TrioNet: Combining Convolution with Local and Global Self-Attention","date":"2021-11-15","arxiv_id":"2111.07547","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-bnas-rethinking-broad-convolutional","title":"Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search","date":"2021-11-15","arxiv_id":"2111.07722","repositories_listed":0,"syntology":null},{"url":null,"slug":"full-attention-based-neural-architecture","title":"Full-attention based Neural Architecture Search using Context Auto-regression","date":"2021-11-13","arxiv_id":"2111.07139","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-one-shot-search-space-poisoning-in","title":"Towards One Shot Search Space Poisoning in Neural Architecture Search","date":"2021-11-13","arxiv_id":"2111.07138","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-approach-for-combining-multimodal-fusion","title":"An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing","date":"2021-11-08","arxiv_id":"2111.04497","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-neural-architecture-search-via","title":"Approximate Neural Architecture Search via Operation Distribution Learning","date":"2021-11-08","arxiv_id":"2111.04670","repositories_listed":0,"syntology":null},{"url":null,"slug":"tnd-nas-towards-non-differentiable-objectives","title":"TND-NAS: Towards Non-differentiable Objectives in Progressive Differentiable NAS Framework","date":"2021-11-06","arxiv_id":"2111.03892","repositories_listed":0,"syntology":null},{"url":null,"slug":"autokd-automatic-knowledge-distillation-into","title":"AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family","date":"2021-11-05","arxiv_id":"2111.03555","repositories_listed":0,"syntology":null},{"url":null,"slug":"rt-rcg-neural-network-and-accelerator-search","title":"RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms","date":"2021-11-04","arxiv_id":"2111.02569","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-separable-neural","title":"Communication-Efficient Separable Neural Network for Distributed Inference on Edge Devices","date":"2021-11-03","arxiv_id":"2111.02489","repositories_listed":0,"syntology":null},{"url":null,"slug":"fitness-landscape-footprint-a-framework-to","title":"Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems","date":"2021-11-02","arxiv_id":"2111.01584","repositories_listed":0,"syntology":null},{"url":null,"slug":"autosumm-automatic-model-creation-for-text","title":"AUTOSUMM: Automatic Model Creation for Text Summarization","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-and-clinically-accurate-head","title":"Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study","date":"2021-11-01","arxiv_id":"2111.01544","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-sleep-stage-classification-using-cnn","title":"A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick","date":"2021-10-27","arxiv_id":"2110.15277","repositories_listed":0,"syntology":null}],"record_sha256":"097db4449cbbfdc6a658a82bb71d4286a19b51511ac2b9d7e5c0d33bfae8ffb1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}