{"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/network-pruning/papers/4","list_of":"/task/network-pruning","task":"Network Pruning","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":4,"pages_in_order":6,"rows_per_page":100,"rows":[301,400],"of":534,"counts":{"archive_papers_tagged":534,"with_a_code_link":239,"where_syntology_ran_a_sample":83,"not_listed_spam_title":0,"listed":534,"listed_where_code_ran":83,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":65,"every_run_a_failure_of_syntologys_instrument":18,"listed_with_a_run_with_no_instrument_failure":65,"listed_every_run_a_failure_of_syntologys_instrument":18,"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/network-pruning","prev":"/task/network-pruning/papers/3","next":"/task/network-pruning/papers/5","papers":[{"url":null,"slug":"unveiling-invariances-via-neural-network","title":"Unveiling Invariances via Neural Network Pruning","date":"2023-09-15","arxiv_id":"2309.08171","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-mask-networks","title":"Ensemble Mask Networks","date":"2023-09-12","arxiv_id":"2309.06382","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-consensus-a-network-pruning-approach","title":"Adaptive Consensus: A network pruning approach for decentralized optimization","date":"2023-09-06","arxiv_id":"2309.02626","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-prune-or-not-to-prune-a-chaos-causality","title":"To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks","date":"2023-08-19","arxiv_id":"2308.09955","repositories_listed":0,"syntology":null},{"url":null,"slug":"pruning-a-neural-network-using-bayesian","title":"Pruning a neural network using Bayesian inference","date":"2023-08-04","arxiv_id":"2308.02451","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-neural-network-pruning-requires","title":"Accurate Neural Network Pruning Requires Rethinking Sparse Optimization","date":"2023-08-03","arxiv_id":"2308.02060","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-compression-methods-for-yolov5-a-review","title":"Model Compression Methods for YOLOv5: A Review","date":"2023-07-21","arxiv_id":"2307.11904","repositories_listed":0,"syntology":null},{"url":null,"slug":"squeezerfacenet-reducing-a-small-face","title":"SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning","date":"2023-07-20","arxiv_id":"2307.10697","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-pruning-as-spectrum-preserving","title":"Neural Network Pruning as Spectrum Preserving Process","date":"2023-07-18","arxiv_id":"2307.08982","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-network-pruning-by-measuring","title":"Structured Network Pruning by Measuring Filter-wise Interactions","date":"2023-07-03","arxiv_id":"2307.00758","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-prune-and-factorize-for-language","title":"Low-Rank Prune-And-Factorize for Language Model Compression","date":"2023-06-25","arxiv_id":"2306.14152","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-pruning-for-real-time-polyp","title":"Neural Network Pruning for Real-time Polyp Segmentation","date":"2023-06-22","arxiv_id":"2306.13203","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-and-decomposition-of-functions","title":"Representation and decomposition of functions in DAG-DNNs and structural network pruning","date":"2023-06-16","arxiv_id":"2306.09707","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-efficient-neural-networks-using","title":"Resource Efficient Neural Networks Using Hessian Based Pruning","date":"2023-06-12","arxiv_id":"2306.07030","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-a-sparse-relu-network-training-problem","title":"Does a sparse ReLU network training problem always admit an optimum?","date":"2023-06-05","arxiv_id":"2306.02666","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmented-random-search-for-multi-objective","title":"Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML","date":"2023-05-23","arxiv_id":"2305.14109","repositories_listed":0,"syntology":null},{"url":null,"slug":"layer-adaptive-structured-pruning-guided-by","title":"Layer-adaptive Structured Pruning Guided by Latency","date":"2023-05-23","arxiv_id":"2305.14403","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-modeling-proving-the-lottery","title":"Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network","date":"2023-05-20","arxiv_id":"2305.12148","repositories_listed":0,"syntology":null},{"url":null,"slug":"concept-monitor-understanding-dnn-training","title":"Concept-Monitor: Understanding DNN training through individual neurons","date":"2023-04-26","arxiv_id":"2304.13346","repositories_listed":0,"syntology":null},{"url":null,"slug":"bias-in-pruned-vision-models-in-depth","title":"Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures","date":"2023-04-25","arxiv_id":"2304.12622","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-pruning-enables-localized-and-efficient","title":"Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing","date":"2023-04-19","arxiv_id":"2304.09876","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-pruning-spaces-1","title":"Network Pruning Spaces","date":"2023-04-19","arxiv_id":"2304.09453","repositories_listed":0,"syntology":null},{"url":null,"slug":"dipnet-efficiency-distillation-and-iterative","title":"DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution","date":"2023-04-14","arxiv_id":"2304.07018","repositories_listed":0,"syntology":null},{"url":null,"slug":"surrogate-lagrangian-relaxation-a-path-to","title":"Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning","date":"2023-04-08","arxiv_id":"2304.04120","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-emo-joint-pruning-with-multiple-sub","title":"A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking","date":"2023-03-28","arxiv_id":"2303.16212","repositories_listed":0,"syntology":null},{"url":null,"slug":"protective-self-adaptive-pruning-to-better","title":"Protective Self-Adaptive Pruning to Better Compress DNNs","date":"2023-03-21","arxiv_id":"2303.11881","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-privacy-meets-neural-network","title":"Differential Privacy Meets Neural Network Pruning","date":"2023-03-08","arxiv_id":"2303.04612","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-enhanced-semi-supervised-federated","title":"Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT","date":"2023-03-05","arxiv_id":"2303.02668","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-sharing-with-network-pruning-for","title":"Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning","date":"2023-03-02","arxiv_id":"2303.00912","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-as-chita-neural-network-pruning-with","title":"Fast as CHITA: Neural Network Pruning with Combinatorial Optimization","date":"2023-02-28","arxiv_id":"2302.14623","repositories_listed":0,"syntology":null},{"url":null,"slug":"less-is-more-the-influence-of-pruning-on-the","title":"Less is More: The Influence of Pruning on the Explainability of CNNs","date":"2023-02-17","arxiv_id":"2302.08878","repositories_listed":0,"syntology":null},{"url":null,"slug":"certified-invertibility-in-neural-networks","title":"Certified Invertibility in Neural Networks via Mixed-Integer Programming","date":"2023-01-27","arxiv_id":"2301.11783","repositories_listed":0,"syntology":null},{"url":null,"slug":"getting-away-with-more-network-pruning-from","title":"Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions","date":"2023-01-19","arxiv_id":"2301.07966","repositories_listed":0,"syntology":null},{"url":null,"slug":"pruning-compact-convnets-for-efficient-1","title":"Pruning Compact ConvNets for Efficient Inference","date":"2023-01-11","arxiv_id":"2301.04502","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-alignment-for-network-pruning","title":"Structural Alignment for Network Pruning through Partial Regularization","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-characterization-of-how-neural","title":"Pruning Before Training May Improve Generalization, Provably","date":"2023-01-01","arxiv_id":"2301.00335","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fairness-aware-adversarial-network","title":"Towards Fairness-aware Adversarial Network Pruning","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"can-we-find-strong-lottery-tickets-in","title":"Can We Find Strong Lottery Tickets in Generative Models?","date":"2022-12-16","arxiv_id":"2212.08311","repositories_listed":0,"syntology":null},{"url":null,"slug":"ap-selective-activation-for-de-sparsifying","title":"AP: Selective Activation for De-sparsifying Pruned Neural Networks","date":"2022-12-09","arxiv_id":"2212.06145","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-learning-rate-schedules-for","title":"Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks","date":"2022-12-09","arxiv_id":"2212.06144","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-stein-variational-inference-for","title":"Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning","date":"2022-12-07","arxiv_id":"2212.03537","repositories_listed":0,"syntology":null},{"url":null,"slug":"attend-who-is-weak-pruning-assisted-medical","title":"Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances","date":"2022-12-06","arxiv_id":"2212.02675","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedtiny-pruned-federated-learning-towards","title":"Distributed Pruning Towards Tiny Neural Networks in Federated Learning","date":"2022-12-05","arxiv_id":"2212.01977","repositories_listed":0,"syntology":null},{"url":null,"slug":"learninggroup-a-real-time-sparse-training-on","title":"LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning","date":"2022-10-29","arxiv_id":"2210.16624","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-consumption-of-neural-networks-on","title":"Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model","date":"2022-10-04","arxiv_id":"2210.01625","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-asr-pathways-a-sparse-multilingual","title":"Learning ASR pathways: A sparse multilingual ASR model","date":"2022-09-13","arxiv_id":"2209.05735","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-network-pruning-at-initialization","title":"One-shot Network Pruning at Initialization with Discriminative Image Patches","date":"2022-09-13","arxiv_id":"2209.05683","repositories_listed":0,"syntology":null},{"url":null,"slug":"cap-instance-complexity-aware-network-pruning","title":"CWP: Instance complexity weighted channel-wise soft masks for network pruning","date":"2022-09-08","arxiv_id":"2209.03534","repositories_listed":0,"syntology":null},{"url":null,"slug":"complexity-driven-cnn-compression-for","title":"Complexity-Driven CNN Compression for Resource-constrained Edge AI","date":"2022-08-26","arxiv_id":"2208.12816","repositories_listed":0,"syntology":null},{"url":null,"slug":"n2nskip-learning-highly-sparse-networks-using","title":"N2NSkip: Learning Highly Sparse Networks using Neuron-to-Neuron Skip Connections","date":"2022-08-07","arxiv_id":"2208.03662","repositories_listed":0,"syntology":null},{"url":null,"slug":"renormalized-sparse-neural-network-pruning","title":"Renormalized Sparse Neural Network Pruning","date":"2022-06-21","arxiv_id":"2206.10088","repositories_listed":0,"syntology":null},{"url":null,"slug":"distortion-aware-network-pruning-and-feature","title":"Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation","date":"2022-06-20","arxiv_id":"2206.09604","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-robustness-lottery-a","title":"\"Understanding Robustness Lottery\": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches","date":"2022-06-16","arxiv_id":"2206.07918","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-network-pruning-for-nuclei","title":"Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images","date":"2022-06-15","arxiv_id":"2206.07422","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-compression-via-effective","title":"Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning","date":"2022-06-07","arxiv_id":"2206.03596","repositories_listed":0,"syntology":null},{"url":null,"slug":"recall-distortion-in-neural-network-pruning","title":"Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm","date":"2022-06-07","arxiv_id":"2206.02976","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-communication-learning-trade-off-for","title":"Towards Communication-Learning Trade-off for Federated Learning at the Network Edge","date":"2022-05-27","arxiv_id":"2205.14271","repositories_listed":0,"syntology":null},{"url":null,"slug":"pruning-has-a-disparate-impact-on-model","title":"Pruning has a disparate impact on model accuracy","date":"2022-05-26","arxiv_id":"2205.13574","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperparameter-optimization-with-neural","title":"Hyperparameter Optimization with Neural Network Pruning","date":"2022-05-18","arxiv_id":"2205.08695","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-block-wise-pruning-with-auxiliary","title":"Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks","date":"2022-05-07","arxiv_id":"2205.03602","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-pruning-by-cooperative","title":"Neural Network Pruning by Cooperative Coevolution","date":"2022-04-12","arxiv_id":"2204.05639","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoadversary-a-pixel-pruning-method-for","title":"AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack","date":"2022-03-18","arxiv_id":"2203.09756","repositories_listed":0,"syntology":null},{"url":null,"slug":"improve-convolutional-neural-network-pruning","title":"Improve Convolutional Neural Network Pruning by Maximizing Filter Variety","date":"2022-03-11","arxiv_id":"2203.05807","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-architecture-slimming-method-for","title":"A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation","date":"2022-02-21","arxiv_id":"2202.10461","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-designing-compact-audio-visual","title":"A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning","date":"2022-02-17","arxiv_id":"2202.08509","repositories_listed":0,"syntology":null},{"url":null,"slug":"prunix-non-ideality-aware-convolutional","title":"PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators","date":"2022-02-03","arxiv_id":"2202.01758","repositories_listed":0,"syntology":null},{"url":null,"slug":"win-the-lottery-ticket-via-fourier-analysis","title":"Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning","date":"2022-01-30","arxiv_id":"2201.12712","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-does-darts-miss-the-target-and-how-do-we","title":"Why Does DARTS Miss the Target, and How Do We Aim to Fix It?","date":"2022-01-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-sparse-connectivity-learning-for","title":"Automatic Sparse Connectivity Learning for Neural Networks","date":"2022-01-13","arxiv_id":"2201.05020","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-lightweight-neural-animation","title":"Towards Lightweight Neural Animation : Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models","date":"2022-01-11","arxiv_id":"2201.04042","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-optimization-for-reinforcement","title":"Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations","date":"2022-01-07","arxiv_id":"2201.02571","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-generalization-and-specialization","title":"Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning","date":"2022-01-06","arxiv_id":"2201.01961","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":"putting-3d-spatially-sparse-networks-on-a","title":"Putting 3D Spatially Sparse Networks on a Diet","date":"2021-12-02","arxiv_id":"2112.01316","repositories_listed":0,"syntology":null},{"url":null,"slug":"validating-the-lottery-ticket-hypothesis-with","title":"Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-approach-to-coreset-learning","title":"A Unified Approach to Coreset Learning","date":"2021-11-04","arxiv_id":"2111.03044","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-pruned-structure-and-weights","title":"Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach","date":"2021-11-01","arxiv_id":"2111.02399","repositories_listed":0,"syntology":null},{"url":null,"slug":"rgp-neural-network-pruning-through-its","title":"RGP: Neural Network Pruning through Its Regular Graph Structure","date":"2021-10-28","arxiv_id":"2110.15192","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-to-prune-a-policy-towards-early","title":"When to Prune? A Policy towards Early Structural Pruning","date":"2021-10-22","arxiv_id":"2110.12007","repositories_listed":0,"syntology":null},{"url":null,"slug":"smof-squeezing-more-out-of-filters-yields","title":"SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning","date":"2021-10-21","arxiv_id":"2110.10842","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-pruning-through-constrained","title":"Neural Network Pruning Through Constrained Reinforcement Learning","date":"2021-10-16","arxiv_id":"2110.08558","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-network-pruning-for","title":"Differentiable Network Pruning for Microcontrollers","date":"2021-10-15","arxiv_id":"2110.08350","repositories_listed":0,"syntology":null},{"url":null,"slug":"compact-cnn-models-for-on-device-ocular-based","title":"Compact CNN Models for On-device Ocular-based User Recognition in Mobile Devices","date":"2021-10-11","arxiv_id":"2110.04953","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-channel-pruning-with-learned","title":"Automated Channel Pruning with Learned Importance","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"can-network-pruning-benefit-deep-learning","title":"Can network pruning benefit deep learning under label noise?","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-ensembles-of-graph-neural-networks","title":"Efficient Ensembles of Graph Neural Networks","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lean-graph-based-pruning-for-convolutional-1","title":"LEAN: graph-based pruning for convolutional neural networks by extracting longest chains","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-efficient-image-super-resolution","title":"Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"network-pruning-optimization-by-simulated","title":"Network Pruning Optimization by Simulated Annealing Algorithm","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-again-the-value-of-network-pruning","title":"Rethinking Again the Value of Network Pruning -- A Dynamical Isometry Perspective","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-pruning-meets-orthogonality","title":"Structured Pruning Meets Orthogonality","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-pattern-pruning-using","title":"Structured Pattern Pruning Using Regularization","date":"2021-09-18","arxiv_id":"2109.08814","repositories_listed":0,"syntology":null},{"url":null,"slug":"gdp-stabilized-neural-network-pruning-via","title":"GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization","date":"2021-09-06","arxiv_id":"2109.02220","repositories_listed":0,"syntology":null},{"url":null,"slug":"multistage-pruning-of-cnn-based-ecg","title":"Multistage Pruning of CNN Based ECG Classifiers for Edge Devices","date":"2021-08-31","arxiv_id":"2109.00516","repositories_listed":0,"syntology":null},{"url":"/paper/threshold-pruning-tool-for-densely-connected","slug":"threshold-pruning-tool-for-densely-connected","title":"New Pruning Method Based on DenseNet Network for Image Classification","date":"2021-08-28","arxiv_id":"2108.12604","repositories_listed":0,"syntology":null},{"url":null,"slug":"basis-scaling-and-double-pruning-for","title":"Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning","date":"2021-08-06","arxiv_id":"2108.02893","repositories_listed":0,"syntology":null},{"url":null,"slug":"provably-efficient-lottery-ticket-discovery","title":"How much pre-training is enough to discover a good subnetwork?","date":"2021-07-31","arxiv_id":"2108.00259","repositories_listed":0,"syntology":null},{"url":null,"slug":"blending-pruning-criteria-for-convolutional","title":"Blending Pruning Criteria for Convolutional Neural Networks","date":"2021-07-11","arxiv_id":"2107.05033","repositories_listed":0,"syntology":null},{"url":null,"slug":"weight-reparametrization-for-budget-aware","title":"Weight Reparametrization for Budget-Aware Network Pruning","date":"2021-07-08","arxiv_id":"2107.03909","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lottery-ticket-hypothesis-framework-for-low","title":"A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification","date":"2021-07-03","arxiv_id":"2107.01461","repositories_listed":0,"syntology":null}],"record_sha256":"3804831515def555f8d27b8bb2f41168f31247260978966d582732dc4858a5f7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}