{"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/apply-transform","entry":"apply_transform","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":7,"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":11,"n_samples_ran":6,"n_samples_fingerprinted":0,"n_places":12,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":1,"ran":4,"unverified":5},"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":"2608.11815","paper":"/paper/arxiv-2608-11815","title":"Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"callous-youth/BMAT","path":"bmat/bmat_attack.py","file_url":"https://github.com/callous-youth/BMAT/blob/HEAD/bmat/bmat_attack.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"752132b1edb824c8","mcp_get_code":{"code_sha256":"752132b1edb824c8"}},{"arxiv_id":"2407.10542","paper":"/paper/3d-geometric-shape-assembly-via-efficient","title":"3D Geometric Shape Assembly via Efficient Point Cloud Matching","date":"2024-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NahyukLEE/pmtr","path":"model/local_global_registration.py","file_url":"https://github.com/NahyukLEE/pmtr/blob/HEAD/model/local_global_registration.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4d9c2ef32c5f322f","mcp_get_code":{"code_sha256":"4d9c2ef32c5f322f"}},{"arxiv_id":"2404.06209","paper":"/paper/elephants-never-forget-memorization-and","title":"Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"interpretml/llm-tabular-memorization-checker","path":"tabmemcheck/datasets/load.py","file_url":"https://github.com/interpretml/llm-tabular-memorization-checker/blob/HEAD/tabmemcheck/datasets/load.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6bd8df53f13381fc","mcp_get_code":{"code_sha256":"6bd8df53f13381fc"}},{"arxiv_id":"2308.06112","paper":"/paper/lip2vec-efficient-and-robust-visual-speech","title":"Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping","date":"2023-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YasserdahouML/Lip2Vec","path":"datasets/preprocess.py","file_url":"https://github.com/YasserdahouML/Lip2Vec/blob/HEAD/datasets/preprocess.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"362b232acbc220b7","mcp_get_code":{"code_sha256":"362b232acbc220b7"}},{"arxiv_id":"2307.12090","paper":"/paper/plantain-diffusion-inspired-pose-score","title":"PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking","date":"2023-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"molecularmodelinglab/plantain","path":"models/plantain.py","file_url":"https://github.com/molecularmodelinglab/plantain/blob/HEAD/models/plantain.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"20b562b8853c66c2","mcp_get_code":{"code_sha256":"20b562b8853c66c2"}},{"arxiv_id":"2204.00008","paper":"/paper/improving-adversarial-transferability-via","title":"Improving Adversarial Transferability via Neuron Attribution-Based Attacks","date":"2022-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anlthms/nips-2017","path":"mmd/defense_mmd.py","file_url":"https://github.com/anlthms/nips-2017/blob/HEAD/mmd/defense_mmd.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"752132b1edb824c8","mcp_get_code":{"code_sha256":"752132b1edb824c8"}},{"arxiv_id":"2107.11992","paper":"/paper/hregnet-a-hierarchical-network-for-large","title":"HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration","date":"2021-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ispc-lab/HRegNet","path":"models/utils.py","file_url":"https://github.com/ispc-lab/HRegNet/blob/HEAD/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4990f46f89fba8bd","mcp_get_code":{"code_sha256":"4990f46f89fba8bd"}},{"arxiv_id":"2010.11202","paper":"/paper/astronomaly-personalised-active-anomaly","title":"Astronomaly: Personalised Active Anomaly Detection in Astronomical Data","date":null,"month_inferred_from_arxiv_id":"2020-10","title_source":"archive","repo":"MichelleLochner/astronomaly","path":"astronomaly/data_management/image_reader.py","file_url":"https://github.com/MichelleLochner/astronomaly/blob/HEAD/astronomaly/data_management/image_reader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"7b0c87b71ac95639","mcp_get_code":{"code_sha256":"7b0c87b71ac95639"}},{"arxiv_id":"1904.09546","paper":"/paper/deepcaps-going-deeper-with-capsule-networks","title":"DeepCaps: Going Deeper with Capsule Networks","date":"2019-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HopefulRational/DeepCaps-PyTorch","path":"deepcaps.py","file_url":"https://github.com/HopefulRational/DeepCaps-PyTorch/blob/HEAD/deepcaps.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e6a27797b1e22e76","mcp_get_code":{"code_sha256":"e6a27797b1e22e76"}},{"arxiv_id":"1811.11168","paper":"/paper/deformable-convnets-v2-more-deformable-better","title":"Deformable ConvNets v2: More Deformable, Better Results","date":"2018-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"4uiiurz1/pytorch-deform-conv-v2","path":"scaled_mnist/dataset.py","file_url":"https://github.com/4uiiurz1/pytorch-deform-conv-v2/blob/HEAD/scaled_mnist/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d821268221317ba7","mcp_get_code":{"code_sha256":"d821268221317ba7"}},{"arxiv_id":"1807.09856","paper":"/paper/where-are-the-blobs-counting-by-localization","title":"Where are the Blobs: Counting by Localization with Point Supervision","date":"2018-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ElementAI/LCFCN","path":"src/datasets/transformers.py","file_url":"https://github.com/ElementAI/LCFCN/blob/HEAD/src/datasets/transformers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"678428d62bcd23a4","mcp_get_code":{"code_sha256":"678428d62bcd23a4"}},{"arxiv_id":"Yue_Object_Recognition_as_Next_Token_Prediction_CVPR_2024_paper","paper":null,"title":"arXiv:Yue_Object_Recognition_as_Next_Token_Prediction_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"kaiyuyue/nxtp","path":"src/loader.py","file_url":"https://github.com/kaiyuyue/nxtp/blob/HEAD/src/loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c4f9365a3b0c3838","mcp_get_code":{"code_sha256":"c4f9365a3b0c3838"}}]}