{"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":"/method/pruning/papers/32","list_of":"/method/pruning","method":"Pruning","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":32,"pages_in_order":39,"rows_per_page":100,"rows":[3101,3200],"of":3874,"counts":{"archive_papers_tagged":3874,"with_a_code_link":1508,"where_syntology_ran_a_sample":478,"not_listed_spam_title":0,"listed":3874,"listed_where_code_ran":478,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":395,"every_run_a_failure_of_syntologys_instrument":83,"listed_with_a_run_with_no_instrument_failure":395,"listed_every_run_a_failure_of_syntologys_instrument":83,"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":"/method/pruning","prev":"/method/pruning/papers/31","next":"/method/pruning/papers/33","papers":[{"paper":null,"slug":"differentiable-joint-pruning-and-quantization","title":"Differentiable Joint Pruning and Quantization for Hardware Efficiency","date":"2020-07-20","arxiv_id":"2007.10463","n_code_links":0,"syntology":null},{"paper":null,"slug":"lottery-tickets-in-linear-models-an-analysis","title":"Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning","date":"2020-07-16","arxiv_id":"2007.08243","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-task-pruning-for-semantic-segmentation","title":"MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks","date":"2020-07-16","arxiv_id":"2007.08386","n_code_links":0,"syntology":null},{"paper":null,"slug":"compression-strategies-and-space-conscious","title":"Compression strategies and space-conscious representations for deep neural networks","date":"2020-07-15","arxiv_id":"2007.07967","n_code_links":0,"syntology":null},{"paper":null,"slug":"reprune-filter-pruning-via-representative","title":"REPrune: Filter Pruning via Representative Election","date":"2020-07-14","arxiv_id":"2007.06932","n_code_links":0,"syntology":null},{"paper":null,"slug":"text-based-rl-agents-with-commonsense-1","title":"Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches","date":"2020-07-12","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/to-filter-prune-or-to-layer-prune-that-is-the","slug":"to-filter-prune-or-to-layer-prune-that-is-the","title":"To Filter Prune, or to Layer Prune, That Is The Question","date":"2020-07-11","arxiv_id":"2007.05667","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-prune-deep-neural-networks-via-2","title":"Learning to Prune Deep Neural Networks via Reinforcement Learning","date":"2020-07-09","arxiv_id":"2007.04756","n_code_links":0,"syntology":null},{"paper":"/paper/neural-architecture-search-with-gbdt","slug":"neural-architecture-search-with-gbdt","title":"Accuracy Prediction with Non-neural Model for Neural Architecture Search","date":"2020-07-09","arxiv_id":"2007.04785","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["renqianluo/GBDT-NAS"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"auto-map-a-dqn-framework-for-exploring","title":"Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads","date":"2020-07-08","arxiv_id":"2007.04069","n_code_links":0,"syntology":null},{"paper":"/paper/binary-stochastic-filtering-feature-selection","slug":"binary-stochastic-filtering-feature-selection","title":"Binary Stochastic Filtering: feature selection and beyond","date":"2020-07-08","arxiv_id":"2007.03920","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-efficient-search-approximation-in","title":"A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP","date":"2020-07-08","arxiv_id":"2007.03948","n_code_links":0,"syntology":null},{"paper":"/paper/operation-aware-soft-channel-pruning-using","slug":"operation-aware-soft-channel-pruning-using","title":"Operation-Aware Soft Channel Pruning using Differentiable Masks","date":"2020-07-08","arxiv_id":"2007.03938","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["kminsoo/SCP"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/riccinets-curvature-guided-pruning-of-high","slug":"riccinets-curvature-guided-pruning-of-high","title":"RicciNets: Curvature-guided Pruning of High-performance Neural Networks Using Ricci Flow","date":"2020-07-08","arxiv_id":"2007.04216","n_code_links":0,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/discretization-aware-architecture-search","slug":"discretization-aware-architecture-search","title":"Discretization-Aware Architecture Search","date":"2020-07-07","arxiv_id":"2007.03154","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":5,"n_instrument":0,"unverified":3,"pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["sunsmarterjie/DAAS"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"enabling-on-device-cnn-training-by-self","title":"Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning","date":"2020-07-07","arxiv_id":"2007.03213","n_code_links":0,"syntology":null},{"paper":"/paper/lossless-cnn-channel-pruning-via-gradient","slug":"lossless-cnn-channel-pruning-via-gradient","title":"ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting","date":"2020-07-07","arxiv_id":"2007.03260","n_code_links":6,"syntology":null},{"paper":null,"slug":"meta-learning-with-network-pruning","title":"Meta-Learning with Network Pruning","date":"2020-07-07","arxiv_id":"2007.03219","n_code_links":0,"syntology":null},{"paper":null,"slug":"bespoke-vs-pret-a-porter-lottery-tickets","title":"Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding","date":"2020-07-06","arxiv_id":"2007.04091","n_code_links":0,"syntology":null},{"paper":"/paper/eagleeye-fast-sub-net-evaluation-for","slug":"eagleeye-fast-sub-net-evaluation-for","title":"EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning","date":"2020-07-06","arxiv_id":"2007.02491","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":2,"n_instrument":1,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["anonymous47823493/EagleEye"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"building-a-competitive-associative-classifier","title":"Building a Competitive Associative Classifier","date":"2020-07-04","arxiv_id":"2007.01972","n_code_links":0,"syntology":null},{"paper":"/paper/dessilbi-exploring-structural-sparsity-of","slug":"dessilbi-exploring-structural-sparsity-of","title":"DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths","date":"2020-07-04","arxiv_id":"2007.02010","n_code_links":1,"syntology":null},{"paper":"/paper/fracbits-mixed-precision-quantization-via","slug":"fracbits-mixed-precision-quantization-via","title":"FracBits: Mixed Precision Quantization via Fractional Bit-Widths","date":"2020-07-04","arxiv_id":"2007.02017","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"weight-dependent-gates-for-network-pruning","title":"Weight-dependent Gates for Network Pruning","date":"2020-07-04","arxiv_id":"2007.02066","n_code_links":0,"syntology":null},{"paper":null,"slug":"active-learning-of-timed-automata-with","title":"Active learning of timed automata with unobservable resets","date":"2020-07-03","arxiv_id":"2007.01637","n_code_links":0,"syntology":null},{"paper":"/paper/interpretable-sequence-classification-via","slug":"interpretable-sequence-classification-via","title":"ProtoryNet - Interpretable Text Classification Via Prototype Trajectories","date":"2020-07-03","arxiv_id":"2007.01777","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-gan-compression-1","slug":"self-supervised-gan-compression-1","title":"Self-Supervised GAN Compression","date":"2020-07-03","arxiv_id":"2007.01491","n_code_links":1,"syntology":null},{"paper":null,"slug":"becoming-linguistically-mature-modeling","title":"Becoming Linguistically Mature: Modeling English and German Children's Writing Development Across School Grades","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"optimisation-of-a-siamese-neural-network-for","title":"Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking","date":"2020-07-01","arxiv_id":"2007.00491","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimisation-of-the-pointpillars-network-for","title":"Optimisation of the PointPillars network for 3D object detection in point clouds","date":"2020-07-01","arxiv_id":"2007.00493","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-shot-structured-pruning-before","title":"Single Shot Structured Pruning Before Training","date":"2020-07-01","arxiv_id":"2007.00389","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-diversity-based-pruning-of","slug":"understanding-diversity-based-pruning-of","title":"Statistical Mechanical Analysis of Neural Network Pruning","date":"2020-06-30","arxiv_id":"2006.16617","n_code_links":1,"syntology":null},{"paper":"/paper/building-rule-hierarchies-for-efficient","slug":"building-rule-hierarchies-for-efficient","title":"Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs","date":"2020-06-29","arxiv_id":"2006.16171","n_code_links":1,"syntology":null},{"paper":"/paper/espn-extremely-sparse-pruned-networks","slug":"espn-extremely-sparse-pruned-networks","title":"ESPN: Extremely Sparse Pruned Networks","date":"2020-06-28","arxiv_id":"2006.15741","n_code_links":1,"syntology":null},{"paper":null,"slug":"data-dependent-pruning-to-find-the-winning","title":"Data-dependent Pruning to find the Winning Lottery Ticket","date":"2020-06-25","arxiv_id":"2006.14350","n_code_links":0,"syntology":null},{"paper":"/paper/neuralscale-efficient-scaling-of-neurons-for-1","slug":"neuralscale-efficient-scaling-of-neurons-for-1","title":"NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks","date":"2020-06-23","arxiv_id":"2006.12813","n_code_links":1,"syntology":null},{"paper":null,"slug":"pfgdf-pruning-filter-via-gaussian","title":"PFGDF: Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration","date":"2020-06-23","arxiv_id":"2006.12963","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploiting-weight-redundancy-in-cnns-beyond","title":"Exploiting Weight Redundancy in CNNs: Beyond Pruning and Quantization","date":"2020-06-22","arxiv_id":"2006.11967","n_code_links":0,"syntology":null},{"paper":"/paper/generative-sparse-detection-networks-for-3d","slug":"generative-sparse-detection-networks-for-3d","title":"Generative Sparse Detection Networks for 3D Single-shot Object Detection","date":"2020-06-22","arxiv_id":"2006.12356","n_code_links":4,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["jgwak/GSDN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"rapid-structural-pruning-of-neural-networks","title":"Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning","date":"2020-06-22","arxiv_id":"2006.12139","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-loss-modelling-for-unstructured","slug":"revisiting-loss-modelling-for-unstructured","title":"Revisiting Loss Modelling for Unstructured Pruning","date":"2020-06-22","arxiv_id":"2006.12279","n_code_links":1,"syntology":null},{"paper":null,"slug":"slimming-neural-networks-using-adaptive","title":"Slimming Neural Networks using Adaptive Connectivity Scores","date":"2020-06-22","arxiv_id":"2006.12463","n_code_links":0,"syntology":null},{"paper":null,"slug":"pruning-the-pilots-deep-learning-based-pilot","title":"Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems","date":"2020-06-21","arxiv_id":"2006.11796","n_code_links":0,"syntology":null},{"paper":"/paper/subspace-clustering-for-action-recognition","slug":"subspace-clustering-for-action-recognition","title":"Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning","date":"2020-06-21","arxiv_id":"2006.11812","n_code_links":1,"syntology":null},{"paper":"/paper/paying-more-attention-to-snapshots-of","slug":"paying-more-attention-to-snapshots-of","title":"Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation","date":"2020-06-20","arxiv_id":"2006.11487","n_code_links":1,"syntology":{"ran":4,"of":8,"n_ran_checked":3,"n_instrument":1,"unverified":4,"pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["lehduong/kesi"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"exploring-weight-importance-and-hessian-bias","title":"Exploring Weight Importance and Hessian Bias in Model Pruning","date":"2020-06-19","arxiv_id":"2006.10903","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-predictability-of-pruning-across","title":"On the Predictability of Pruning Across Scales","date":"2020-06-18","arxiv_id":"2006.10621","n_code_links":0,"syntology":null},{"paper":"/paper/directional-pruning-of-deep-neural-networks","slug":"directional-pruning-of-deep-neural-networks","title":"Directional Pruning of Deep Neural Networks","date":"2020-06-16","arxiv_id":"2006.09358","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["donlan2710/gRDA-Optimizer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"measuring-model-complexity-of-neural-networks","title":"Measuring Model Complexity of Neural Networks with Curve Activation Functions","date":"2020-06-16","arxiv_id":"2006.08962","n_code_links":0,"syntology":null},{"paper":"/paper/progressive-skeletonization-trimming-more-fat","slug":"progressive-skeletonization-trimming-more-fat","title":"Progressive Skeletonization: Trimming more fat from a network at initialization","date":"2020-06-16","arxiv_id":"2006.09081","n_code_links":1,"syntology":{"ran":5,"of":14,"n_ran_checked":5,"n_instrument":0,"unverified":9,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","official":{"repos":["naver/force"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":9,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"real-time-universal-style-transfer-on-high","title":"Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning","date":"2020-06-16","arxiv_id":"2006.09029","n_code_links":0,"syntology":null},{"paper":"/paper/apq-joint-search-for-network-architecture-1","slug":"apq-joint-search-for-network-architecture-1","title":"APQ: Joint Search for Network Architecture, Pruning and Quantization Policy","date":"2020-06-15","arxiv_id":"2006.08509","n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-graph-attention-networks-using-effective","title":"Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification","date":"2020-06-15","arxiv_id":"2006.08796","n_code_links":0,"syntology":null},{"paper":"/paper/finding-trainable-sparse-networks-through","slug":"finding-trainable-sparse-networks-through","title":"Finding trainable sparse networks through Neural Tangent Transfer","date":"2020-06-15","arxiv_id":"2006.08228","n_code_links":1,"syntology":null},{"paper":"/paper/generalized-optimal-sparse-decision-trees","slug":"generalized-optimal-sparse-decision-trees","title":"Generalized and Scalable Optimal Sparse Decision Trees","date":"2020-06-15","arxiv_id":"2006.08690","n_code_links":2,"syntology":{"ran":8,"of":9,"n_ran_checked":8,"n_instrument":0,"unverified":1,"pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Jimmy-Lin/GeneralizedOptimalSparseDecisionTrees"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/gnnguard-defending-graph-neural-networks","slug":"gnnguard-defending-graph-neural-networks","title":"GNNGuard: Defending Graph Neural Networks against Adversarial Attacks","date":"2020-06-15","arxiv_id":"2006.08149","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["mims-harvard/GNNGuard"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/neural-network-compression-using-higher-order","slug":"neural-network-compression-using-higher-order","title":"Neural Network Compression Using Higher-Order Statistics and AuxiliaryReconstruction Losses","date":"2020-06-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/now-that-i-can-see-i-can-improve-enabling","slug":"now-that-i-can-see-i-can-improve-enabling","title":"Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge","date":"2020-06-15","arxiv_id":"2006.08554","n_code_links":1,"syntology":null},{"paper":"/paper/optimal-lottery-tickets-via-subsetsum","slug":"optimal-lottery-tickets-via-subsetsum","title":"Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient","date":"2020-06-14","arxiv_id":"2006.07990","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":null}},{"paper":null,"slug":"dagger-a-python-framework-for-reproducible","title":"dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration","date":"2020-06-12","arxiv_id":"2006.07484","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-model-pruning-with-feedback-1","title":"Dynamic Model Pruning with Feedback","date":"2020-06-12","arxiv_id":"2006.07253","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-design-space-exploration-for","title":"Automated Design Space Exploration for optimised Deployment of DNN on Arm Cortex-A CPUs","date":"2020-06-09","arxiv_id":"2006.05181","n_code_links":0,"syntology":null},{"paper":"/paper/pruning-neural-networks-without-any-data-by","slug":"pruning-neural-networks-without-any-data-by","title":"Pruning neural networks without any data by iteratively conserving synaptic flow","date":"2020-06-09","arxiv_id":"2006.05467","n_code_links":6,"syntology":{"ran":12,"of":17,"n_ran_checked":11,"n_instrument":1,"unverified":5,"pointer_only":16,"phrase":"12 ran (of which 5 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["ganguli-lab/Synaptic-Flow"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/a-framework-for-neural-network-pruning-using","slug":"a-framework-for-neural-network-pruning-using","title":"A Framework for Neural Network Pruning Using Gibbs Distributions","date":"2020-06-08","arxiv_id":"2006.04981","n_code_links":1,"syntology":null},{"paper":null,"slug":"approximate-learning-of-high-dimensional","title":"Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets","date":"2020-06-08","arxiv_id":"2006.04753","n_code_links":0,"syntology":null},{"paper":null,"slug":"novel-adaptive-binary-search-strategy-first","title":"Novel Adaptive Binary Search Strategy-First Hybrid Pyramid- and Clustering-Based CNN Filter Pruning Method without Parameters Setting","date":"2020-06-08","arxiv_id":"2006.04451","n_code_links":0,"syntology":null},{"paper":null,"slug":"admp-an-adversarial-double-masks-based","title":"ADMP: An Adversarial Double Masks Based Pruning Framework For Unsupervised Cross-Domain Compression","date":"2020-06-07","arxiv_id":"2006.04127","n_code_links":0,"syntology":null},{"paper":"/paper/edropout-energy-based-dropout-and-pruning-of","slug":"edropout-energy-based-dropout-and-pruning-of","title":"EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks","date":"2020-06-07","arxiv_id":"2006.04270","n_code_links":2,"syntology":null},{"paper":null,"slug":"sparse-learning-with-cart","title":"Sparse learning with CART","date":"2020-06-07","arxiv_id":"2006.04266","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-natural-language-understanding","slug":"accelerating-natural-language-understanding","title":"Accelerating Natural Language Understanding in Task-Oriented Dialog","date":"2020-06-05","arxiv_id":"2006.03701","n_code_links":1,"syntology":null},{"paper":"/paper/solving-hard-ai-planning-instances-using","slug":"solving-hard-ai-planning-instances-using","title":"Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning","date":"2020-06-04","arxiv_id":"2006.02689","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":null}},{"paper":"/paper/weight-pruning-via-adaptive-sparsity-loss","slug":"weight-pruning-via-adaptive-sparsity-loss","title":"Weight Pruning via Adaptive Sparsity Loss","date":"2020-06-04","arxiv_id":"2006.02768","n_code_links":1,"syntology":null},{"paper":"/paper/shapley-value-as-principled-metric-for","slug":"shapley-value-as-principled-metric-for","title":"Shapley Value as Principled Metric for Structured Network Pruning","date":"2020-06-02","arxiv_id":"2006.01795","n_code_links":1,"syntology":null},{"paper":null,"slug":"discrete-model-compression-with-resource","title":"Discrete Model Compression With Resource Constraint for Deep Neural Networks","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-filter-pruning-criteria-for-deep","title":"Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-dimensional-pruning-a-unified-framework","title":"Multi-Dimensional Pruning: A Unified Framework for Model Compression","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/pruning-via-iterative-ranking-of-sensitivity","slug":"pruning-via-iterative-ranking-of-sensitivity","title":"Pruning via Iterative Ranking of Sensitivity Statistics","date":"2020-06-01","arxiv_id":"2006.00896","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["StijnVerdenius/SNIP-it"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"scalable-top-k-query-on-information-networks","title":"Scalable Top-k Query on Information Networks with Hierarchical Inheritance Relations","date":"2020-06-01","arxiv_id":"2006.01279","n_code_links":0,"syntology":null},{"paper":null,"slug":"complexity-reduction-of-volterra-nonlinear","title":"Complexity Reduction of Volterra Nonlinear Equalization for Optical Short-Reach IM/DD Systems","date":"2020-05-29","arxiv_id":"2005.14453","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-feature-map-discriminant-perspective-for","title":"A Feature-map Discriminant Perspective for Pruning Deep Neural Networks","date":"2020-05-28","arxiv_id":"2005.13796","n_code_links":0,"syntology":null},{"paper":null,"slug":"local-motion-planner-for-autonomous","title":"Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy","date":"2020-05-26","arxiv_id":"2005.12815","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-neural-networks-at-scale-a","title":"Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study","date":"2020-05-23","arxiv_id":"2005.11619","n_code_links":0,"syntology":null},{"paper":"/paper/position-based-scaled-gradient-for-model","slug":"position-based-scaled-gradient-for-model","title":"Position-based Scaled Gradient for Model Quantization and Pruning","date":"2020-05-22","arxiv_id":"2005.11035","n_code_links":1,"syntology":null},{"paper":null,"slug":"prunenet-channel-pruning-via-global","title":"PruneNet: Channel Pruning via Global Importance","date":"2020-05-22","arxiv_id":"2005.11282","n_code_links":0,"syntology":null},{"paper":null,"slug":"cpot-channel-pruning-via-optimal-transport","title":"CPOT: Channel Pruning via Optimal Transport","date":"2020-05-21","arxiv_id":"2005.10451","n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-statistics-guided-efficient-filter","title":"Feature Statistics Guided Efficient Filter Pruning","date":"2020-05-21","arxiv_id":"2005.12193","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-performance-estimation-in-neural","slug":"rethinking-performance-estimation-in-neural","title":"Rethinking Performance Estimation in Neural Architecture Search","date":"2020-05-20","arxiv_id":"2005.09917","n_code_links":1,"syntology":null},{"paper":"/paper/tinylstms-efficient-neural-speech-enhancement","slug":"tinylstms-efficient-neural-speech-enhancement","title":"TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids","date":"2020-05-20","arxiv_id":"2005.11138","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-technique-combining-image-processing","title":"A Novel Technique Combining Image Processing, Plant Development Properties, and the Hungarian Algorithm, to Improve Leaf Detection in Maize","date":"2020-05-18","arxiv_id":"2005.09022","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-multi-dimension-pruning","title":"Joint Multi-Dimension Pruning via Numerical Gradient Update","date":"2020-05-18","arxiv_id":"2005.08931","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-sparsity-neural-networks-for","title":"Dynamic Sparsity Neural Networks for Automatic Speech Recognition","date":"2020-05-16","arxiv_id":"2005.10627","n_code_links":0,"syntology":null},{"paper":null,"slug":"hnas-hierarchical-neural-architecture-search","title":"Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search","date":"2020-05-15","arxiv_id":"2005.07564","n_code_links":0,"syntology":null},{"paper":"/paper/movement-pruning-adaptive-sparsity-by-fine","slug":"movement-pruning-adaptive-sparsity-by-fine","title":"Movement Pruning: Adaptive Sparsity by Fine-Tuning","date":"2020-05-15","arxiv_id":"2005.07683","n_code_links":4,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["huggingface/transformers"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/bayesian-bits-unifying-quantization-and","slug":"bayesian-bits-unifying-quantization-and","title":"Bayesian Bits: Unifying Quantization and Pruning","date":"2020-05-14","arxiv_id":"2005.07093","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/dynamic-sparse-training-find-efficient-sparse-1","slug":"dynamic-sparse-training-find-efficient-sparse-1","title":"Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers","date":"2020-05-14","arxiv_id":"2005.06870","n_code_links":1,"syntology":null},{"paper":null,"slug":"prive-hd-privacy-preserved-hyperdimensional","title":"Prive-HD: Privacy-Preserved Hyperdimensional Computing","date":"2020-05-14","arxiv_id":"2005.06716","n_code_links":0,"syntology":null},{"paper":null,"slug":"artificial-neural-network-pruning-to-extract","title":"Pruning coupled with learning, ensembles of minimal neural networks, and future of XAI","date":"2020-05-13","arxiv_id":"2005.06284","n_code_links":0,"syntology":null},{"paper":null,"slug":"compact-neural-representation-using-attentive","title":"Compact Neural Representation Using Attentive Network Pruning","date":"2020-05-10","arxiv_id":"2005.04559","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpu-acceleration-of-sparse-neural-networks","title":"GPU Acceleration of Sparse Neural Networks","date":"2020-05-09","arxiv_id":"2005.04347","n_code_links":0,"syntology":null},{"paper":null,"slug":"pruning-algorithms-to-accelerate","title":"Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey","date":"2020-05-08","arxiv_id":"2005.04275","n_code_links":0,"syntology":null}],"record_sha256":"8b7f6c1d4e334553279ec966f2370c41bb63d72009a19a872e254e9de6d1a746","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}