{"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/9","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":9,"pages_in_order":39,"rows_per_page":100,"rows":[801,900],"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/8","next":"/method/pruning/papers/10","papers":[{"paper":"/paper/vltp-vision-language-guided-token-pruning-for","slug":"vltp-vision-language-guided-token-pruning-for","title":"VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation","date":"2024-09-13","arxiv_id":"2409.08464","n_code_links":1,"syntology":null},{"paper":null,"slug":"structured-pruning-for-efficient-visual-place","title":"Structured Pruning for Efficient Visual Place Recognition","date":"2024-09-12","arxiv_id":"2409.07834","n_code_links":0,"syntology":null},{"paper":"/paper/hesso-towards-automatic-efficient-and-user","slug":"hesso-towards-automatic-efficient-and-user","title":"HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning","date":"2024-09-11","arxiv_id":"2409.09085","n_code_links":1,"syntology":null},{"paper":null,"slug":"how-redundant-is-the-transformer-stack-in","title":"How Redundant Is the Transformer Stack in Speech Representation Models?","date":"2024-09-10","arxiv_id":"2409.16302","n_code_links":0,"syntology":null},{"paper":null,"slug":"mcdgln-masked-connection-based-dynamic-graph","title":"MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder","date":"2024-09-10","arxiv_id":"2409.06163","n_code_links":0,"syntology":null},{"paper":"/paper/sara-high-efficient-diffusion-model-fine","slug":"sara-high-efficient-diffusion-model-fine","title":"SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation","date":"2024-09-10","arxiv_id":"2409.06633","n_code_links":1,"syntology":null},{"paper":null,"slug":"stun-structured-then-unstructured-pruning-for","title":"STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning","date":"2024-09-10","arxiv_id":"2409.06211","n_code_links":0,"syntology":null},{"paper":null,"slug":"federated-transfer-learning-based-cooperative","title":"Federated Transfer Learning Based Cooperative Wideband Spectrum Sensing with Model Pruning","date":"2024-09-09","arxiv_id":"2409.05462","n_code_links":0,"syntology":null},{"paper":"/paper/bbs-bi-directional-bit-level-sparsity-for","slug":"bbs-bi-directional-bit-level-sparsity-for","title":"BBS: Bi-directional Bit-level Sparsity for Deep Learning Acceleration","date":"2024-09-08","arxiv_id":"2409.05227","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-intrinsic-language-specific","slug":"exploring-intrinsic-language-specific","title":"Exploring Intrinsic Language-specific Subspaces in Fine-tuning Multilingual Neural Machine Translation","date":"2024-09-08","arxiv_id":"2409.05224","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["spike0924/lslo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/property-neurons-in-self-supervised-speech","slug":"property-neurons-in-self-supervised-speech","title":"Property Neurons in Self-Supervised Speech Transformers","date":"2024-09-07","arxiv_id":"2409.05910","n_code_links":1,"syntology":null},{"paper":"/paper/how-do-your-code-llms-perform-empowering-code","slug":"how-do-your-code-llms-perform-empowering-code","title":"How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data","date":"2024-09-05","arxiv_id":"2409.03810","n_code_links":1,"syntology":null},{"paper":null,"slug":"in-search-of-trees-decision-tree-policy","title":"In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Search","date":"2024-09-05","arxiv_id":"2409.03260","n_code_links":0,"syntology":null},{"paper":null,"slug":"tropnnc-structured-neural-network-compression","title":"TropNNC: Structured Neural Network Compression Using Tropical Geometry","date":"2024-09-05","arxiv_id":"2409.03945","n_code_links":0,"syntology":null},{"paper":null,"slug":"pre-training-data-selection-for-biomedical","title":"Pre-training data selection for biomedical domain adaptation using journal impact metrics","date":"2024-09-04","arxiv_id":"2409.02725","n_code_links":0,"syntology":null},{"paper":"/paper/2409-13694","slug":"2409-13694","title":"Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study","date":"2024-09-03","arxiv_id":"2409.13694","n_code_links":2,"syntology":null},{"paper":null,"slug":"leveraging-large-language-models-for-solving","title":"Leveraging Large Language Models for Solving Rare MIP Challenges","date":"2024-09-03","arxiv_id":"2409.04464","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantifying-emergence-in-neural-networks","title":"Quantifying Emergence in Neural Networks: Insights from Pruning and Training Dynamics","date":"2024-09-03","arxiv_id":"2409.01568","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-pruning-a-survey-and-benchmark-of","slug":"adversarial-pruning-a-survey-and-benchmark-of","title":"Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness","date":"2024-09-02","arxiv_id":"2409.01249","n_code_links":1,"syntology":null},{"paper":null,"slug":"balancing-performance-and-efficiency-a","title":"Balancing Performance and Efficiency: A Multimodal Large Language Model Pruning Method based Image Text Interaction","date":"2024-09-02","arxiv_id":"2409.01162","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-efficiency-molecular-data-pruning-for","title":"Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization","date":"2024-09-02","arxiv_id":"2409.01081","n_code_links":0,"syntology":null},{"paper":null,"slug":"edge-ai-evaluation-of-model-compression","title":"Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks","date":"2024-09-02","arxiv_id":"2409.02134","n_code_links":0,"syntology":null},{"paper":"/paper/recoverable-compression-a-multimodal-vision","slug":"recoverable-compression-a-multimodal-vision","title":"Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information","date":"2024-09-02","arxiv_id":"2409.01179","n_code_links":2,"syntology":{"ran":8,"of":10,"n_ran_checked":4,"n_instrument":4,"unverified":2,"pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","official":{"repos":["banjiuyufen/recoverablecompression","banjiuyufen/Recoverable-Compression"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"revisiting-smoe-language-models-by-evaluating","title":"Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning","date":"2024-09-02","arxiv_id":"2409.01483","n_code_links":0,"syntology":null},{"paper":"/paper/contextcite-attributing-model-generation-to","slug":"contextcite-attributing-model-generation-to","title":"ContextCite: Attributing Model Generation to Context","date":"2024-09-01","arxiv_id":"2409.00729","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 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) · 1 unverified","official":{"repos":["madrylab/context-cite"],"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":null,"slug":"modularity-in-transformers-investigating","title":"Modularity in Transformers: Investigating Neuron Separability & Specialization","date":"2024-08-30","arxiv_id":"2408.17324","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-iterative-optimal-brain-surgeon-faster","title":"The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information","date":"2024-08-30","arxiv_id":"2408.17163","n_code_links":0,"syntology":null},{"paper":null,"slug":"creating-a-segmented-pointcloud-of-grapevines","title":"Creating a Segmented Pointcloud of Grapevines by Combining Multiple Viewpoints Through Visual Odometry","date":"2024-08-29","arxiv_id":"2408.16472","n_code_links":0,"syntology":null},{"paper":null,"slug":"pse-net-channel-pruning-for-convolutional","title":"PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator","date":"2024-08-29","arxiv_id":"2408.16233","n_code_links":0,"syntology":null},{"paper":null,"slug":"revisit-micro-batch-clipping-adaptive-data","title":"Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation","date":"2024-08-29","arxiv_id":"2408.16204","n_code_links":0,"syntology":null},{"paper":null,"slug":"emp-enhance-memory-in-data-pruning","title":"EMP: Enhance Memory in Data Pruning","date":"2024-08-28","arxiv_id":"2408.16031","n_code_links":0,"syntology":null},{"paper":"/paper/free-lunch-in-the-forest-functionally","slug":"free-lunch-in-the-forest-functionally","title":"Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles","date":"2024-08-28","arxiv_id":"2408.16167","n_code_links":2,"syntology":null},{"paper":null,"slug":"fusing-pruned-and-backdoored-models-optimal","title":"Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation","date":"2024-08-28","arxiv_id":"2408.15861","n_code_links":0,"syntology":null},{"paper":"/paper/channel-wise-influence-estimating-data","slug":"channel-wise-influence-estimating-data","title":"Channel-wise Influence: Estimating Data Influence for Multivariate Time Series","date":"2024-08-27","arxiv_id":"2408.14763","n_code_links":0,"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":null}},{"paper":"/paper/learning-effective-pruning-at-initialization","slug":"learning-effective-pruning-at-initialization","title":"Learning effective pruning at initialization from iterative pruning","date":"2024-08-27","arxiv_id":"2408.14757","n_code_links":1,"syntology":null},{"paper":"/paper/pat-pruning-aware-tuning-for-large-language","slug":"pat-pruning-aware-tuning-for-large-language","title":"PAT: Pruning-Aware Tuning for Large Language Models","date":"2024-08-27","arxiv_id":"2408.14721","n_code_links":2,"syntology":null},{"paper":"/paper/1-bit-fqt-pushing-the-limit-of-fully","slug":"1-bit-fqt-pushing-the-limit-of-fully","title":"1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit","date":"2024-08-26","arxiv_id":"2408.14267","n_code_links":1,"syntology":null},{"paper":"/paper/3d-point-cloud-network-pruning-when-some","slug":"3d-point-cloud-network-pruning-when-some","title":"3D Point Cloud Network Pruning: When Some Weights Do not Matter","date":"2024-08-26","arxiv_id":"2408.14601","n_code_links":1,"syntology":null},{"paper":null,"slug":"adaptive-resolution-inference-ari-energy","title":"Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things","date":"2024-08-26","arxiv_id":"2408.14528","n_code_links":0,"syntology":null},{"paper":null,"slug":"hapm-hardware-aware-pruning-method-for-cnn","title":"HAPM -- Hardware Aware Pruning Method for CNN hardware accelerators in resource constrained devices","date":"2024-08-26","arxiv_id":"2408.14055","n_code_links":0,"syntology":null},{"paper":null,"slug":"kgprune-a-web-application-to-extract","title":"KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning","date":"2024-08-26","arxiv_id":"2408.14658","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-specific-calibration-for-pruning","title":"Investigating Language-Specific Calibration For Pruning Multilingual Large Language Models","date":"2024-08-26","arxiv_id":"2408.14398","n_code_links":0,"syntology":null},{"paper":"/paper/count-based-novelty-exploration-in-classical","slug":"count-based-novelty-exploration-in-classical","title":"Count-based Novelty Exploration in Classical Planning","date":"2024-08-25","arxiv_id":"2408.13719","n_code_links":1,"syntology":null},{"paper":null,"slug":"variational-autoencoder-based-neural-network","title":"Variational autoencoder-based neural network model compression","date":"2024-08-25","arxiv_id":"2408.14513","n_code_links":0,"syntology":null},{"paper":null,"slug":"mpruner-optimizing-neural-network-size-with","title":"MPruner: Optimizing Neural Network Size with CKA-Based Mutual Information Pruning","date":"2024-08-24","arxiv_id":"2408.13482","n_code_links":0,"syntology":null},{"paper":null,"slug":"growing-deep-neural-network-considering-with","title":"Growing Deep Neural Network Considering with Similarity between Neurons","date":"2024-08-23","arxiv_id":"2408.13291","n_code_links":0,"syntology":null},{"paper":"/paper/qadaprune-adaptive-parameter-pruning-for","slug":"qadaprune-adaptive-parameter-pruning-for","title":"QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits","date":"2024-08-23","arxiv_id":"2408.13352","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-ultimate-guide-to-fine-tuning-llms-from","title":"The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities","date":"2024-08-23","arxiv_id":"2408.13296","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-greedy-hierarchical-approach-to-whole","title":"A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs","date":"2024-08-22","arxiv_id":"2409.03777","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-valid-dual-bounds-in-constraint","title":"Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning","date":"2024-08-22","arxiv_id":"2408.12695","n_code_links":0,"syntology":null},{"paper":null,"slug":"not-all-samples-should-be-utilized-equally","title":"Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation","date":"2024-08-22","arxiv_id":"2408.12483","n_code_links":0,"syntology":null},{"paper":null,"slug":"smartphone-based-eye-tracking-system-using","title":"Smartphone-based Eye Tracking System using Edge Intelligence and Model Optimisation","date":"2024-08-22","arxiv_id":"2408.12463","n_code_links":0,"syntology":null},{"paper":null,"slug":"trex-reusing-vision-transformer-s-attention","title":"TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing","date":"2024-08-22","arxiv_id":"2408.12742","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-pruning-and-distillation-in-practice-the","title":"LLM Pruning and Distillation in Practice: The Minitron Approach","date":"2024-08-21","arxiv_id":"2408.11796","n_code_links":0,"syntology":null},{"paper":null,"slug":"practical-token-pruning-for-foundation-models","title":"Practical token pruning for foundation models in few-shot conversational virtual assistant systems","date":"2024-08-21","arxiv_id":"2408.11799","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-one-shot-pruned-pre-trained","title":"Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism","date":"2024-08-20","arxiv_id":"2408.10473","n_code_links":0,"syntology":null},{"paper":"/paper/llm-barber-block-aware-rebuilder-for-sparsity","slug":"llm-barber-block-aware-rebuilder-for-sparsity","title":"LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models","date":"2024-08-20","arxiv_id":"2408.10631","n_code_links":1,"syntology":null},{"paper":null,"slug":"near-field-multiuser-communications-aided-by","title":"Near-Field Multiuser Communications Aided by Movable Antennas","date":"2024-08-20","arxiv_id":"2408.10552","n_code_links":0,"syntology":null},{"paper":"/paper/antidote-post-fine-tuning-safety-alignment","slug":"antidote-post-fine-tuning-safety-alignment","title":"Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning","date":"2024-08-18","arxiv_id":"2408.09600","n_code_links":3,"syntology":{"ran":3,"of":5,"n_ran_checked":2,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/beyond-kan-introducing-karsein-for-adaptive","slug":"beyond-kan-introducing-karsein-for-adaptive","title":"CTR-KAN: KAN for Adaptive High-Order Feature Interaction Modeling","date":"2024-08-16","arxiv_id":"2408.08713","n_code_links":1,"syntology":null},{"paper":null,"slug":"convexity-based-pruning-of-speech","title":"Convexity-based Pruning of Speech Representation Models","date":"2024-08-16","arxiv_id":"2408.11858","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-completeness-of-conflict-based-search","title":"On the Completeness of Conflict-Based Search: Temporally-Relative Duplicate Pruning","date":"2024-08-16","arxiv_id":"2408.09028","n_code_links":0,"syntology":null},{"paper":null,"slug":"research-on-personalized-compression","title":"Research on Personalized Compression Algorithm for Pre-trained Models Based on Homomorphic Entropy Increase","date":"2024-08-16","arxiv_id":"2408.08684","n_code_links":0,"syntology":null},{"paper":"/paper/corradaptor-adaptive-local-context-learning","slug":"corradaptor-adaptive-local-context-learning","title":"CorrAdaptor: Adaptive Local Context Learning for Correspondence Pruning","date":"2024-08-15","arxiv_id":"2408.08134","n_code_links":1,"syntology":null},{"paper":"/paper/pqv-mobile-a-combined-pruning-and","slug":"pqv-mobile-a-combined-pruning-and","title":"PQV-Mobile: A Combined Pruning and Quantization Toolkit to Optimize Vision Transformers for Mobile Applications","date":"2024-08-15","arxiv_id":"2408.08437","n_code_links":1,"syntology":null},{"paper":null,"slug":"random-gradient-masking-as-a-defensive","title":"Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning","date":"2024-08-15","arxiv_id":"2408.08430","n_code_links":0,"syntology":null},{"paper":"/paper/surgical-sam-2-real-time-segment-anything-in","slug":"surgical-sam-2-real-time-segment-anything-in","title":"Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning","date":"2024-08-15","arxiv_id":"2408.07931","n_code_links":1,"syntology":{"ran":13,"of":16,"n_ran_checked":8,"n_instrument":5,"unverified":3,"pointer_only":4,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 1 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["jinlab-imvr/surgical-sam-2"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"an-effective-information-theoretic-framework","title":"An Effective Information Theoretic Framework for Channel Pruning","date":"2024-08-14","arxiv_id":"2408.16772","n_code_links":0,"syntology":null},{"paper":"/paper/con-fold-explainable-machine-learning-with","slug":"con-fold-explainable-machine-learning-with","title":"CON-FOLD -- Explainable Machine Learning with Confidence","date":"2024-08-14","arxiv_id":"2408.07854","n_code_links":1,"syntology":null},{"paper":null,"slug":"infra-yolo-efficient-neural-network-structure","title":"Infra-YOLO: Efficient Neural Network Structure with Model Compression for Real-Time Infrared Small Object Detection","date":"2024-08-14","arxiv_id":"2408.07455","n_code_links":0,"syntology":null},{"paper":null,"slug":"alpha-trimming-locally-adaptive-tree-pruning","title":"Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests","date":"2024-08-13","arxiv_id":"2408.07151","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-exclusion-of-low-fidelity-data-in","title":"Dynamic Exclusion of Low-Fidelity Data in Bayesian Optimization for Autonomous Beamline Alignment","date":"2024-08-13","arxiv_id":"2408.06540","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-sd-edge-cloud-collaborative-inference","title":"Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models","date":"2024-08-13","arxiv_id":"2408.06646","n_code_links":0,"syntology":null},{"paper":null,"slug":"lora-2-multi-scale-low-rank-approximations","title":"LoRA$^2$ : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models","date":"2024-08-13","arxiv_id":"2408.06854","n_code_links":0,"syntology":null},{"paper":"/paper/token-compensator-altering-inference-cost-of","slug":"token-compensator-altering-inference-cost-of","title":"Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning","date":"2024-08-13","arxiv_id":"2408.06798","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-federated-learning-using-dynamic","title":"Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data","date":"2024-08-11","arxiv_id":"2408.05678","n_code_links":0,"syntology":null},{"paper":null,"slug":"p3-a-policy-driven-pace-adaptive-and","title":"P3: A Policy-Driven, Pace-Adaptive, and Diversity-Promoted Framework for data pruning in LLM Training","date":"2024-08-10","arxiv_id":"2408.05541","n_code_links":0,"syntology":null},{"paper":null,"slug":"confident-magnitude-based-neural-network","title":"Confident magnitude-based neural network pruning","date":"2024-08-08","arxiv_id":"2408.04759","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-convex-optimization-based-layer-wise-post","title":"A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models","date":"2024-08-07","arxiv_id":"2408.03728","n_code_links":0,"syntology":null},{"paper":null,"slug":"adapmtl-adaptive-pruning-framework-for","title":"AdapMTL: Adaptive Pruning Framework for Multitask Learning Model","date":"2024-08-07","arxiv_id":"2408.03913","n_code_links":0,"syntology":null},{"paper":null,"slug":"prism-progressive-dependency-maximization-for","title":"PRISM: PRogressive dependency maxImization for Scale-invariant image Matching","date":"2024-08-07","arxiv_id":"2408.03598","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-02961","title":"Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks","date":"2024-08-06","arxiv_id":"2408.02961","n_code_links":0,"syntology":null},{"paper":"/paper/2408-03046","slug":"2408-03046","title":"Comb, Prune, Distill: Towards Unified Pruning for Vision Model Compression","date":"2024-08-06","arxiv_id":"2408.03046","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-02341","title":"An approach to optimize inference of the DIART speaker diarization pipeline","date":"2024-08-05","arxiv_id":"2408.02341","n_code_links":0,"syntology":null},{"paper":"/paper/2408-02760","slug":"2408-02760","title":"Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series Analysis","date":"2024-08-05","arxiv_id":"2408.02760","n_code_links":1,"syntology":null},{"paper":"/paper/selective-pruning-and-neuronal-death-generate","slug":"selective-pruning-and-neuronal-death-generate","title":"Selective pruning and neuronal death generate heavy-tail network connectivity","date":"2024-08-05","arxiv_id":"2408.02625","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-01765","title":"Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning","date":"2024-08-03","arxiv_id":"2408.01765","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-01173","title":"Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems","date":"2024-08-02","arxiv_id":"2408.01173","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-tiny-supervised-odl-core-with-auto-data","title":"A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition","date":"2024-08-02","arxiv_id":"2408.01283","n_code_links":0,"syntology":null},{"paper":"/paper/2407-21316","slug":"2407-21316","title":"Diff-Cleanse: Identifying and Mitigating Backdoor Attacks in Diffusion Models","date":"2024-07-31","arxiv_id":"2407.21316","n_code_links":1,"syntology":null},{"paper":"/paper/investigating-sparsity-in-recurrent-neural","slug":"investigating-sparsity-in-recurrent-neural","title":"Investigating Sparsity in Recurrent Neural Networks","date":"2024-07-30","arxiv_id":"2407.20601","n_code_links":1,"syntology":null},{"paper":"/paper/pruning-large-language-models-with-semi","slug":"pruning-large-language-models-with-semi","title":"Pruning Large Language Models with Semi-Structural Adaptive Sparse Training","date":"2024-07-30","arxiv_id":"2407.20584","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["thu-ml/adaptive-sparse-trainer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/think-thinner-key-cache-by-query-driven","slug":"think-thinner-key-cache-by-query-driven","title":"ThinK: Thinner Key Cache by Query-Driven Pruning","date":"2024-07-30","arxiv_id":"2407.21018","n_code_links":0,"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":null}},{"paper":null,"slug":"toward-efficient-permutation-for-hierarchical","title":"Toward Efficient Permutation for Hierarchical N:M Sparsity on GPUs","date":"2024-07-30","arxiv_id":"2407.20496","n_code_links":0,"syntology":null},{"paper":null,"slug":"realizing-unaligned-block-wise-pruning-for","title":"Realizing Unaligned Block-wise Pruning for DNN Acceleration on Mobile Devices","date":"2024-07-29","arxiv_id":"2407.19644","n_code_links":0,"syntology":null},{"paper":null,"slug":"memory-efficient-training-of-llms-with-larger","title":"Mini-batch Coresets for Memory-efficient Training of Large Language Models","date":"2024-07-28","arxiv_id":"2407.19580","n_code_links":0,"syntology":null},{"paper":null,"slug":"greedy-output-approximation-towards-efficient","title":"Greedy Output Approximation: Towards Efficient Structured Pruning for LLMs Without Retraining","date":"2024-07-26","arxiv_id":"2407.19126","n_code_links":0,"syntology":null},{"paper":"/paper/topology-optimization-of-random-memristors","slug":"topology-optimization-of-random-memristors","title":"Topology Optimization of Random Memristors for Input-Aware Dynamic SNN","date":"2024-07-26","arxiv_id":"2407.18625","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-inference-of-vision-instruction","slug":"efficient-inference-of-vision-instruction","title":"Efficient Inference of Vision Instruction-Following Models with Elastic Cache","date":"2024-07-25","arxiv_id":"2407.18121","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":2,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["liuzuyan/elasticcache"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"pruning-boolean-d-dnnf-circuits-through","title":"Pruning Boolean d-DNNF Circuits Through Tseitin-Awareness","date":"2024-07-25","arxiv_id":"2407.17951","n_code_links":0,"syntology":null}],"record_sha256":"bfe7b2dc80303d15b342cce4e725f3f72c4e0015598ba4f3f30b7d5913733f38","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}