{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/code/regularization","entry":"regularization","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":12,"n_papers_ran":6,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":8,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":12,"n_places_pointer_only":3,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":1,"unverified":4},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2506.05019","paper":"/paper/finmultitime-a-four-modal-bilingual-dataset","title":"FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysis","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":"archive","repo":"marigoldwu/pydgc","path":"pydgc/models/dgcluster.py","file_url":"https://github.com/marigoldwu/pydgc/blob/HEAD/pydgc/models/dgcluster.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e82410e993da99a0","mcp_get_code":{"code_sha256":"e82410e993da99a0"}},{"arxiv_id":"2405.14246","paper":"/paper/gcondenser-benchmarking-graph-condensation","title":"GCondenser: Benchmarking Graph Condensation","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"superallen13/GCondenser","path":"graph_condenser/models/sgdd.py","file_url":"https://github.com/superallen13/GCondenser/blob/HEAD/graph_condenser/models/sgdd.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68ba9924e5b4f043","mcp_get_code":{"code_sha256":"68ba9924e5b4f043"}},{"arxiv_id":"2211.12875","paper":"/paper/a-survey-of-deep-graph-clustering-taxonomy","title":"A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource","date":"2022-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"marigoldwu/a-unified-framework-for-deep-attribute-graph-clustering","path":"pydgc/models/dgcluster.py","file_url":"https://github.com/marigoldwu/a-unified-framework-for-deep-attribute-graph-clustering/blob/HEAD/pydgc/models/dgcluster.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e82410e993da99a0","mcp_get_code":{"code_sha256":"e82410e993da99a0"}},{"arxiv_id":"2201.03016","paper":"/paper/learning-class-prototypes-from-synthetic","title":"Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection","date":"2022-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"orion-ai-lab/prototypeinsar","path":"Models.py","file_url":"https://github.com/orion-ai-lab/prototypeinsar/blob/HEAD/Models.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f59c2ee46a72cac8","mcp_get_code":{"code_sha256":"f59c2ee46a72cac8"}},{"arxiv_id":"2105.14636","paper":"/paper/mlpruning-a-multilevel-structured-pruning","title":"LEAP: Learnable Pruning for Transformer-based Models","date":"2021-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaozhewei/mlpruning","path":"training/masked_run_glue.py","file_url":"https://github.com/yaozhewei/mlpruning/blob/HEAD/training/masked_run_glue.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"729d19d073f5c736","mcp_get_code":{"code_sha256":"729d19d073f5c736"}},{"arxiv_id":"2005.07683","paper":"/paper/movement-pruning-adaptive-sparsity-by-fine","title":"Movement Pruning: Adaptive Sparsity by Fine-Tuning","date":"2020-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"1e24f40610cc5f59","mcp_get_code":{"code_sha256":"1e24f40610cc5f59"}},{"arxiv_id":"1911.04933","paper":"/paper/eternal-sunshine-of-the-spotless-net","title":"Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks","date":"2019-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zero-or-one/URP","path":"learning/learn_utils.py","file_url":"https://github.com/zero-or-one/URP/blob/HEAD/learning/learn_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4079cde5d6b5a1af","mcp_get_code":{"code_sha256":"4079cde5d6b5a1af"}},{"arxiv_id":"1910.06188","paper":"/paper/q8bert-quantized-8bit-bert","title":"Q8BERT: Quantized 8Bit BERT","date":"2019-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huggingface/block_movement_pruning","path":"block_movement_pruning/masked_run_squad.py","file_url":"https://github.com/huggingface/block_movement_pruning/blob/HEAD/block_movement_pruning/masked_run_squad.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e24f40610cc5f59","mcp_get_code":{"code_sha256":"1e24f40610cc5f59"}},{"arxiv_id":"1906.05661","paper":"/paper/training-neural-networks-for-and-by","title":"Training Neural Networks for and by Interpolation","date":"2019-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oval-group/ali-g","path":"experiments/utils.py","file_url":"https://github.com/oval-group/ali-g/blob/HEAD/experiments/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46bc8b0a1b81b718","mcp_get_code":{"code_sha256":"46bc8b0a1b81b718"}},{"arxiv_id":"1811.07591","paper":"/paper/deep-frank-wolfe-for-neural-network","title":"Deep Frank-Wolfe For Neural Network Optimization","date":"2018-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oval-group/dfw","path":"experiments/utils.py","file_url":"https://github.com/oval-group/dfw/blob/HEAD/experiments/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46bc8b0a1b81b718","mcp_get_code":{"code_sha256":"46bc8b0a1b81b718"}},{"arxiv_id":"1805.03438","paper":"/paper/robust-classification-with-convolutional","title":"Robust Classification with Convolutional Prototype Learning","date":"2018-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shivgahlout/Robust-Classification-with-Convolutional-Prototype-Learning-Pytorch","path":"Models.py","file_url":"https://github.com/shivgahlout/Robust-Classification-with-Convolutional-Prototype-Learning-Pytorch/blob/HEAD/Models.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f59c2ee46a72cac8","mcp_get_code":{"code_sha256":"f59c2ee46a72cac8"}},{"arxiv_id":"1710.10903","paper":"/paper/graph-attention-networks","title":"Graph Attention Networks","date":"2017-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joaopedromattos/gnee","path":"event_graph_utils.py","file_url":"https://github.com/joaopedromattos/gnee/blob/HEAD/event_graph_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"725414c560fb8a82","mcp_get_code":{"code_sha256":"725414c560fb8a82"}}]}