{"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/6","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":6,"pages_in_order":39,"rows_per_page":100,"rows":[501,600],"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/5","next":"/method/pruning/papers/7","papers":[{"paper":"/paper/exploring-glu-expansion-ratios-a-study-of","slug":"exploring-glu-expansion-ratios-a-study-of","title":"Exploring GLU Expansion Ratios: A Study of Structured Pruning in LLaMA-3.2 Models","date":"2024-12-26","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"resource-efficient-transformer-architecture","title":"Resource-Efficient Transformer Architecture: Optimizing Memory and Execution Time for Real-Time Applications","date":"2024-12-25","arxiv_id":"2501.00042","n_code_links":0,"syntology":null},{"paper":null,"slug":"autosculpt-a-pattern-based-model-auto-pruning","title":"AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning","date":"2024-12-24","arxiv_id":"2412.18091","n_code_links":0,"syntology":null},{"paper":null,"slug":"pruning-unrolled-networks-pun-at","title":"Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization","date":"2024-12-24","arxiv_id":"2412.18668","n_code_links":0,"syntology":null},{"paper":null,"slug":"slimgpt-layer-wise-structured-pruning-for","title":"SlimGPT: Layer-wise Structured Pruning for Large Language Models","date":"2024-12-24","arxiv_id":"2412.18110","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-stochastic-framework-for-neural","title":"Unified Stochastic Framework for Neural Network Quantization and Pruning","date":"2024-12-24","arxiv_id":"2412.18184","n_code_links":0,"syntology":null},{"paper":null,"slug":"gqsa-group-quantization-and-sparsity-for","title":"GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference","date":"2024-12-23","arxiv_id":"2412.17560","n_code_links":0,"syntology":null},{"paper":"/paper/singular-value-scaling-efficient-generative","slug":"singular-value-scaling-efficient-generative","title":"Singular Value Scaling: Efficient Generative Model Compression via Pruned Weights Refinement","date":"2024-12-23","arxiv_id":"2412.17387","n_code_links":1,"syntology":null},{"paper":null,"slug":"scalable-speech-enhancement-with-dynamic","title":"Scalable Speech Enhancement with Dynamic Channel Pruning","date":"2024-12-22","arxiv_id":"2412.17121","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-compression-via-low","title":"Lillama: Large Language Models Compression via Low-Rank Feature Distillation","date":"2024-12-21","arxiv_id":"2412.16719","n_code_links":0,"syntology":null},{"paper":null,"slug":"less-is-more-towards-green-code-large","title":"Less is More: Towards Green Code Large Language Models via Unified Structural Pruning","date":"2024-12-20","arxiv_id":"2412.15921","n_code_links":0,"syntology":null},{"paper":"/paper/prunevid-visual-token-pruning-for-efficient","slug":"prunevid-visual-token-pruning-for-efficient","title":"PruneVid: Visual Token Pruning for Efficient Video Large Language Models","date":"2024-12-20","arxiv_id":"2412.16117","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":["visual-ai/prunevid"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"adacred-adaptive-causal-decision-transformers","title":"AdaCred: Adaptive Causal Decision Transformers with Feature Crediting","date":"2024-12-19","arxiv_id":"2412.15427","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-pruning-for-large-language-models","title":"Adaptive Pruning for Large Language Models with Structural Importance Awareness","date":"2024-12-19","arxiv_id":"2412.15127","n_code_links":0,"syntology":null},{"paper":null,"slug":"all-in-one-tuning-and-structural-pruning-for","title":"All-in-One Tuning and Structural Pruning for Domain-Specific LLMs","date":"2024-12-19","arxiv_id":"2412.14426","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-fine-tuning-and-concept-suppression","slug":"efficient-fine-tuning-and-concept-suppression","title":"Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models","date":"2024-12-19","arxiv_id":"2412.15341","n_code_links":1,"syntology":null},{"paper":null,"slug":"holistic-adversarially-robust-pruning","title":"Holistic Adversarially Robust Pruning","date":"2024-12-19","arxiv_id":"2412.14714","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-federated-learning-in-the-face-of","title":"Robust Federated Learning in the Face of Covariate Shift: A Magnitude Pruning with Hybrid Regularization Framework for Enhanced Model Aggregation","date":"2024-12-19","arxiv_id":"2412.15010","n_code_links":0,"syntology":null},{"paper":"/paper/yolov11-optimization-for-efficient-resource","slug":"yolov11-optimization-for-efficient-resource","title":"YOLOv11 Optimization for Efficient Resource Utilization","date":"2024-12-19","arxiv_id":"2412.14790","n_code_links":1,"syntology":null},{"paper":null,"slug":"dreamark-rooting-watermark-in-score","title":"DreaMark: Rooting Watermark in Score Distillation Sampling Generated Neural Radiance Fields","date":"2024-12-18","arxiv_id":"2412.15278","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-compression-of-language-models-for","title":"On the Compression of Language Models for Code: An Empirical Study on CodeBERT","date":"2024-12-18","arxiv_id":"2412.13737","n_code_links":0,"syntology":null},{"paper":null,"slug":"resource-constrained-pathfinding-with","title":"Resource Constrained Pathfinding with Enhanced Bidirectional A* Search","date":"2024-12-18","arxiv_id":"2412.13888","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-and-analyzing-model-robustness","title":"Understanding and Analyzing Model Robustness and Knowledge-Transfer in Multilingual Neural Machine Translation using TX-Ray","date":"2024-12-18","arxiv_id":"2412.13881","n_code_links":0,"syntology":null},{"paper":"/paper/4drgs-4d-radiative-gaussian-splatting-for","slug":"4drgs-4d-radiative-gaussian-splatting-for","title":"4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images","date":"2024-12-17","arxiv_id":"2412.12919","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparative-study-of-pruning-methods-in","title":"A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting","date":"2024-12-17","arxiv_id":"2412.12883","n_code_links":0,"syntology":null},{"paper":null,"slug":"activating-distributed-visual-region-within","title":"Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference","date":"2024-12-17","arxiv_id":"2412.12785","n_code_links":0,"syntology":null},{"paper":"/paper/faster-vision-mamba-is-rebuilt-in-minutes-via","slug":"faster-vision-mamba-is-rebuilt-in-minutes-via","title":"Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training","date":"2024-12-17","arxiv_id":"2412.12496","n_code_links":1,"syntology":null},{"paper":null,"slug":"feather-the-throttle-revisiting-visual-token","title":"Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration","date":"2024-12-17","arxiv_id":"2412.13180","n_code_links":0,"syntology":null},{"paper":null,"slug":"hypergs-hyperspectral-3d-gaussian-splatting","title":"HyperGS: Hyperspectral 3D Gaussian Splatting","date":"2024-12-17","arxiv_id":"2412.12849","n_code_links":0,"syntology":null},{"paper":"/paper/itp-instance-aware-test-pruning-for-out-of","slug":"itp-instance-aware-test-pruning-for-out-of","title":"ITP: Instance-Aware Test Pruning for Out-of-Distribution Detection","date":"2024-12-17","arxiv_id":"2412.12566","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-coarse-to-fine-pruning-of-graph","title":"Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition","date":"2024-12-17","arxiv_id":"2412.12887","n_code_links":0,"syntology":null},{"paper":null,"slug":"more-tokens-lower-precision-towards-the","title":"More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression","date":"2024-12-17","arxiv_id":"2412.12706","n_code_links":0,"syntology":null},{"paper":null,"slug":"numerical-pruning-for-efficient","title":"Numerical Pruning for Efficient Autoregressive Models","date":"2024-12-17","arxiv_id":"2412.12441","n_code_links":0,"syntology":null},{"paper":"/paper/rctrans-radar-camera-transformer-via-radar","slug":"rctrans-radar-camera-transformer-via-radar","title":"RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection","date":"2024-12-17","arxiv_id":"2412.12799","n_code_links":1,"syntology":null},{"paper":"/paper/remotetrimmer-adaptive-structural-pruning-for","slug":"remotetrimmer-adaptive-structural-pruning-for","title":"RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification","date":"2024-12-17","arxiv_id":"2412.12603","n_code_links":1,"syntology":null},{"paper":"/paper/structural-pruning-via-spatial-aware","slug":"structural-pruning-via-spatial-aware","title":"Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation","date":"2024-12-17","arxiv_id":"2412.12672","n_code_links":1,"syntology":null},{"paper":"/paper/beyond-graph-convolution-multimodal","slug":"beyond-graph-convolution-multimodal","title":"Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs","date":"2024-12-16","arxiv_id":"2412.11747","n_code_links":1,"syntology":null},{"paper":null,"slug":"designing-semi-structured-pruning-of-graph","title":"Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition","date":"2024-12-16","arxiv_id":"2412.11813","n_code_links":0,"syntology":null},{"paper":null,"slug":"ftp-a-fine-grained-token-wise-pruner-for","title":"FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing","date":"2024-12-16","arxiv_id":"2412.11494","n_code_links":0,"syntology":null},{"paper":null,"slug":"qpruner-probabilistic-decision-quantization","title":"QPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models","date":"2024-12-16","arxiv_id":"2412.11629","n_code_links":0,"syntology":null},{"paper":"/paper/retrollm-empowering-large-language-models-to","slug":"retrollm-empowering-large-language-models-to","title":"RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation","date":"2024-12-16","arxiv_id":"2412.11919","n_code_links":1,"syntology":null},{"paper":"/paper/scalable-temporal-anomaly-causality-discovery","slug":"scalable-temporal-anomaly-causality-discovery","title":"Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data","date":"2024-12-16","arxiv_id":"2412.11800","n_code_links":1,"syntology":null},{"paper":"/paper/speechprune-context-aware-token-pruning-for","slug":"speechprune-context-aware-token-pruning-for","title":"SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval","date":"2024-12-16","arxiv_id":"2412.12009","n_code_links":1,"syntology":null},{"paper":null,"slug":"trimllm-progressive-layer-dropping-for-domain","title":"TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs","date":"2024-12-15","arxiv_id":"2412.11242","n_code_links":0,"syntology":null},{"paper":"/paper/tinysubnets-an-efficient-and-low-capacity","slug":"tinysubnets-an-efficient-and-low-capacity","title":"TinySubNets: An efficient and low capacity continual learning strategy","date":"2024-12-14","arxiv_id":"2412.10869","n_code_links":1,"syntology":null},{"paper":"/paper/data-pruning-can-do-more-a-comprehensive-data","slug":"data-pruning-can-do-more-a-comprehensive-data","title":"Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identification","date":"2024-12-13","arxiv_id":"2412.10091","n_code_links":1,"syntology":null},{"paper":null,"slug":"mesha-efficient-path-planing-with-motion","title":"MeshA*: Efficient Path Planing With Motion Primitives","date":"2024-12-13","arxiv_id":"2412.10320","n_code_links":0,"syntology":null},{"paper":null,"slug":"mvq-towards-efficient-dnn-compression-and","title":"MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization","date":"2024-12-13","arxiv_id":"2412.10261","n_code_links":0,"syntology":null},{"paper":null,"slug":"splinegs-robust-motion-adaptive-spline-for","title":"SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D Gaussians from Monocular Video","date":"2024-12-13","arxiv_id":"2412.09982","n_code_links":0,"syntology":null},{"paper":null,"slug":"static-pruning-in-dense-retrieval-using","title":"Static Pruning in Dense Retrieval using Matrix Decomposition","date":"2024-12-13","arxiv_id":"2412.09983","n_code_links":0,"syntology":null},{"paper":"/paper/tsgaussian-semantic-and-depth-guided-target","slug":"tsgaussian-semantic-and-depth-guided-target","title":"TSGaussian: Semantic and Depth-Guided Target-Specific Gaussian Splatting from Sparse Views","date":"2024-12-13","arxiv_id":"2412.10051","n_code_links":1,"syntology":{"ran":10,"of":12,"n_ran_checked":10,"n_instrument":0,"unverified":2,"pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["leon2000-ai/tsgaussian"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/fast-track-to-winning-tickets-repowering-one","slug":"fast-track-to-winning-tickets-repowering-one","title":"Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural Networks","date":"2024-12-10","arxiv_id":"2412.07605","n_code_links":1,"syntology":null},{"paper":null,"slug":"mobile-video-diffusion","title":"Mobile Video Diffusion","date":"2024-12-10","arxiv_id":"2412.07583","n_code_links":0,"syntology":null},{"paper":"/paper/post-training-statistical-calibration-for","slug":"post-training-statistical-calibration-for","title":"Post-Training Statistical Calibration for Higher Activation Sparsity","date":"2024-12-10","arxiv_id":"2412.07174","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"pointer_only":0,"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":["intellabs/scap"],"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":"score-matching-based-structure-learning-for","title":"Score-matching-based Structure Learning for Temporal Data on Networks","date":"2024-12-10","arxiv_id":"2412.07469","n_code_links":0,"syntology":null},{"paper":null,"slug":"tt-mpd-test-time-model-pruning-and","title":"TT-MPD: Test Time Model Pruning and Distillation","date":"2024-12-10","arxiv_id":"2412.07114","n_code_links":0,"syntology":null},{"paper":null,"slug":"federated-split-learning-with-model-pruning","title":"Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks","date":"2024-12-09","arxiv_id":"2412.06414","n_code_links":0,"syntology":null},{"paper":"/paper/illava-an-image-is-worth-fewer-than-1-3-input","slug":"illava-an-image-is-worth-fewer-than-1-3-input","title":"iLLaVA: An Image is Worth Fewer Than 1/3 Input Tokens in Large Multimodal Models","date":"2024-12-09","arxiv_id":"2412.06263","n_code_links":1,"syntology":null},{"paper":null,"slug":"llm-bip-structured-pruning-for-large-language","title":"LLM-BIP: Structured Pruning for Large Language Models with Block-Wise Forward Importance Propagation","date":"2024-12-09","arxiv_id":"2412.06419","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-how-iterative-magnitude-pruning-discovers","title":"On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks","date":"2024-12-09","arxiv_id":"2412.06545","n_code_links":0,"syntology":null},{"paper":null,"slug":"pruning-all-rounder-rethinking-and-improving","title":"Pruning All-Rounder: Rethinking and Improving Inference Efficiency for Large Vision Language Models","date":"2024-12-09","arxiv_id":"2412.06458","n_code_links":0,"syntology":null},{"paper":null,"slug":"safewatch-an-efficient-safety-policy","title":"SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations","date":"2024-12-09","arxiv_id":"2412.06878","n_code_links":0,"syntology":null},{"paper":"/paper/cls-token-tells-everything-needed-for","slug":"cls-token-tells-everything-needed-for","title":"[CLS] Token Tells Everything Needed for Training-free Efficient MLLMs","date":"2024-12-08","arxiv_id":"2412.05819","n_code_links":1,"syntology":{"ran":9,"of":9,"n_ran_checked":4,"n_instrument":5,"unverified":0,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","official":{"repos":["thu-mig/vtc-cls"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/dapperfl-domain-adaptive-federated-learning","slug":"dapperfl-domain-adaptive-federated-learning","title":"DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices","date":"2024-12-08","arxiv_id":"2412.05823","n_code_links":1,"syntology":{"ran":8,"of":14,"n_ran_checked":5,"n_instrument":3,"unverified":6,"pointer_only":2,"phrase":"8 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; 3 where Syntology's instrument failed) · 6 unverified","official":{"repos":["jyzgh/dapperfl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/flexdit-dynamic-token-density-control-for","slug":"flexdit-dynamic-token-density-control-for","title":"FlexDiT: Dynamic Token Density Control for Diffusion Transformer","date":"2024-12-08","arxiv_id":"2412.06028","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":6,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","official":{"repos":["changsn/FlexDiT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"adaptive-dropout-for-pruning-conformers","title":"Adaptive Dropout for Pruning Conformers","date":"2024-12-06","arxiv_id":"2412.04836","n_code_links":0,"syntology":null},{"paper":"/paper/cross-self-kv-cache-pruning-for-efficient","slug":"cross-self-kv-cache-pruning-for-efficient","title":"Cross-Self KV Cache Pruning for Efficient Vision-Language Inference","date":"2024-12-05","arxiv_id":"2412.04652","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":1,"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":{"repos":["terrypei/csp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"2dgs-room-seed-guided-2d-gaussian-splatting","title":"2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains for High-Fidelity Indoor Scene Reconstruction","date":"2024-12-04","arxiv_id":"2412.03428","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-granger-causal-perspective-on-gradient","title":"A Granger-Causal Perspective on Gradient Descent with Application to Pruning","date":"2024-12-04","arxiv_id":"2412.03035","n_code_links":0,"syntology":null},{"paper":"/paper/a-stitch-in-time-saves-nine-small-vlm-is-a","slug":"a-stitch-in-time-saves-nine-small-vlm-is-a","title":"A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs","date":"2024-12-04","arxiv_id":"2412.03324","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["NUS-HPC-AI-Lab/SGL"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"paper":"/paper/aim-adaptive-inference-of-multi-modal-llms","slug":"aim-adaptive-inference-of-multi-modal-llms","title":"AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning","date":"2024-12-04","arxiv_id":"2412.03248","n_code_links":1,"syntology":{"ran":9,"of":14,"n_ran_checked":5,"n_instrument":4,"unverified":5,"pointer_only":3,"phrase":"9 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; 4 where Syntology's instrument failed) · 5 unverified","official":{"repos":["lavi-lab/aim"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"designing-dnns-for-a-trade-off-between","title":"Designing DNNs for a trade-off between robustness and processing performance in embedded devices","date":"2024-12-04","arxiv_id":"2412.03682","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-single-event-upsets-in-deep-neural","slug":"evaluating-single-event-upsets-in-deep-neural","title":"Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective","date":"2024-12-04","arxiv_id":"2412.03630","n_code_links":2,"syntology":null},{"paper":null,"slug":"unifying-kv-cache-compression-for-large","title":"Unifying KV Cache Compression for Large Language Models with LeanKV","date":"2024-12-04","arxiv_id":"2412.03131","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-model-compression-techniques-with","title":"Efficient Model Compression Techniques with FishLeg","date":"2024-12-03","arxiv_id":"2412.02328","n_code_links":0,"syntology":null},{"paper":null,"slug":"effortless-efficiency-low-cost-pruning-of","title":"Effortless Efficiency: Low-Cost Pruning of Diffusion Models","date":"2024-12-03","arxiv_id":"2412.02852","n_code_links":0,"syntology":null},{"paper":null,"slug":"6dope-gs-online-6d-object-pose-estimation","title":"6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting","date":"2024-12-02","arxiv_id":"2412.01543","n_code_links":0,"syntology":null},{"paper":"/paper/cls-attention-is-all-you-need-for-training","slug":"cls-attention-is-all-you-need-for-training","title":"Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs","date":"2024-12-02","arxiv_id":"2412.01818","n_code_links":2,"syntology":{"ran":9,"of":9,"n_ran_checked":5,"n_instrument":4,"unverified":0,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["theia-4869/fastervlm","theia-4869/vispruner"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"efficient-llm-inference-using-dynamic-input","title":"Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking","date":"2024-12-02","arxiv_id":"2412.01380","n_code_links":0,"syntology":null},{"paper":null,"slug":"hdgs-textured-2d-gaussian-splatting-for","title":"HDGS: Textured 2D Gaussian Splatting for Enhanced Scene Rendering","date":"2024-12-02","arxiv_id":"2412.01823","n_code_links":0,"syntology":null},{"paper":null,"slug":"research-on-optimizing-real-time-data","title":"Research on Optimizing Real-Time Data Processing in High-Frequency Trading Algorithms using Machine Learning","date":"2024-12-02","arxiv_id":"2412.01062","n_code_links":0,"syntology":null},{"paper":"/paper/tinyfusion-diffusion-transformers-learned","slug":"tinyfusion-diffusion-transformers-learned","title":"TinyFusion: Diffusion Transformers Learned Shallow","date":"2024-12-02","arxiv_id":"2412.01199","n_code_links":1,"syntology":{"ran":4,"of":8,"n_ran_checked":2,"n_instrument":2,"unverified":4,"pointer_only":8,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["vainf/tinyfusion"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/token-cropr-faster-vits-for-quite-a-few-tasks","slug":"token-cropr-faster-vits-for-quite-a-few-tasks","title":"Token Cropr: Faster ViTs for Quite a Few Tasks","date":"2024-12-01","arxiv_id":"2412.00965","n_code_links":1,"syntology":null},{"paper":null,"slug":"atp-llava-adaptive-token-pruning-for-large","title":"ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models","date":"2024-11-30","arxiv_id":"2412.00447","n_code_links":0,"syntology":null},{"paper":"/paper/pruned-convolutional-attention-network-based","slug":"pruned-convolutional-attention-network-based","title":"Pruned Convolutional Attention Network Based Wideband Spectrum Sensing with Sub-Nyquist Sampling","date":"2024-11-30","arxiv_id":"2412.00562","n_code_links":1,"syntology":null},{"paper":"/paper/speedy-splat-fast-3d-gaussian-splatting-with","slug":"speedy-splat-fast-3d-gaussian-splatting-with","title":"Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives","date":"2024-11-30","arxiv_id":"2412.00578","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["j-alex-hanson/speedy-splat"],"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":null,"slug":"non-linear-equalization-in-112-gb-s-pons","title":"Non-linear Equalization in 112 Gb/s PONs Using Kolmogorov-Arnold Networks","date":"2024-11-29","arxiv_id":"2411.19631","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-set-distillation-information","title":"Video Set Distillation: Information Diversification and Temporal Densification","date":"2024-11-28","arxiv_id":"2412.00111","n_code_links":0,"syntology":null},{"paper":null,"slug":"individual-content-and-motion-dynamics","title":"Individual Content and Motion Dynamics Preserved Pruning for Video Diffusion Models","date":"2024-11-27","arxiv_id":"2411.18375","n_code_links":0,"syntology":null},{"paper":null,"slug":"preserving-deep-representations-in-one-shot","title":"Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework","date":"2024-11-27","arxiv_id":"2411.18376","n_code_links":0,"syntology":null},{"paper":null,"slug":"preserving-information-how-does-topological","title":"Preserving Information: How does Topological Data Analysis improve Neural Network performance?","date":"2024-11-27","arxiv_id":"2411.18410","n_code_links":0,"syntology":null},{"paper":null,"slug":"pruning-deep-convolutional-neural-network","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","date":"2024-11-27","arxiv_id":"2411.18578","n_code_links":0,"syntology":null},{"paper":"/paper/training-noise-token-pruning","slug":"training-noise-token-pruning","title":"Training Noise Token Pruning","date":"2024-11-27","arxiv_id":"2411.18092","n_code_links":1,"syntology":null},{"paper":"/paper/clover-constrained-learning-with-orthonormal","slug":"clover-constrained-learning-with-orthonormal","title":"CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning","date":"2024-11-26","arxiv_id":"2411.17426","n_code_links":1,"syntology":null},{"paper":"/paper/distractor-free-generalizable-3d-gaussian","slug":"distractor-free-generalizable-3d-gaussian","title":"Distractor-free Generalizable 3D Gaussian Splatting","date":"2024-11-26","arxiv_id":"2411.17605","n_code_links":1,"syntology":null},{"paper":null,"slug":"scalable-iterative-pruning-of-large-language","title":"Scalable iterative pruning of large language and vision models using block coordinate descent","date":"2024-11-26","arxiv_id":"2411.17796","n_code_links":0,"syntology":null},{"paper":"/paper/training-a-neural-netwok-for-data-reduction","slug":"training-a-neural-netwok-for-data-reduction","title":"Training a neural netwok for data reduction and better generalization","date":"2024-11-26","arxiv_id":"2411.17180","n_code_links":1,"syntology":null},{"paper":null,"slug":"curvature-in-the-looking-glass-optimal","title":"Curvature in the Looking-Glass: Optimal Methods to Exploit Curvature of Expectation in the Loss Landscape","date":"2024-11-25","arxiv_id":"2411.16914","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-convolutional-neural-networks-structured","title":"Deep Convolutional Neural Networks Structured Pruning via Gravity Regularization","date":"2024-11-25","arxiv_id":"2411.16901","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-lineage-inference-uncovering-privacy","title":"Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning","date":"2024-11-24","arxiv_id":"2411.15796","n_code_links":0,"syntology":null}],"record_sha256":"5acc522e9d628e1514c2db27c976cfbc72b1c93e397a850c1746361a0c1e91d3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}