{"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/get-grads","entry":"get_grads","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":13,"n_papers_ran":5,"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":12,"n_samples_ran":5,"n_samples_fingerprinted":1,"n_places":13,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":2,"unverified":7},"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":"2605.19607","paper":"/paper/arxiv-2605-19607","title":"Spectral Integrated Gradients for Coarse-to-Fine Feature Attribution","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"leekwoon/sig","path":"cleanig/explainer/spectral_ig.py","file_url":"https://github.com/leekwoon/sig/blob/HEAD/cleanig/explainer/spectral_ig.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8fd0d6c205dcc0f2","mcp_get_code":{"code_sha256":"8fd0d6c205dcc0f2"}},{"arxiv_id":"2605.08464","paper":"/paper/arxiv-2605-08464","title":"The Geometric Structure of Models Learning Sparse Data","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"ThomasWalker1/RFAM","path":"utils/rfm.py","file_url":"https://github.com/ThomasWalker1/RFAM/blob/HEAD/utils/rfm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d43a7623958fe7e6","mcp_get_code":{"code_sha256":"d43a7623958fe7e6"}},{"arxiv_id":"2410.22113","paper":"/paper/where-do-large-learning-rates-lead-us","title":"Where Do Large Learning Rates Lead Us?","date":"2024-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isadrtdinov/understanding-large-lrs","path":"synthetic_example/model.py","file_url":"https://github.com/isadrtdinov/understanding-large-lrs/blob/HEAD/synthetic_example/model.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":"84a8ef25008d233d","mcp_get_code":{"code_sha256":"84a8ef25008d233d"}},{"arxiv_id":"2405.05349","paper":"/paper/offline-model-based-optimization-via-policy","title":"Offline Model-Based Optimization via Policy-Guided Gradient Search","date":"2024-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yassineCh/PGS","path":"generate_trajectories.py","file_url":"https://github.com/yassineCh/PGS/blob/HEAD/generate_trajectories.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"753958f65f38b74a","mcp_get_code":{"code_sha256":"753958f65f38b74a"}},{"arxiv_id":"2312.12232","paper":"/paper/brush-your-text-synthesize-any-scene-text-on","title":"Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model","date":"2023-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ecnuljzhang/brush-your-text","path":"controlnet_util/Textgen/poisson_reconstruct.py","file_url":"https://github.com/ecnuljzhang/brush-your-text/blob/HEAD/controlnet_util/Textgen/poisson_reconstruct.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"48688abc93b004ef","mcp_get_code":{"code_sha256":"48688abc93b004ef"}},{"arxiv_id":"2206.07737","paper":"/paper/disparate-impact-in-differential-privacy-from","title":"Disparate Impact in Differential Privacy from Gradient Misalignment","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"layer6ai-labs/fair-dp","path":"utils.py","file_url":"https://github.com/layer6ai-labs/fair-dp/blob/HEAD/utils.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":"cfc5de3ebf0a6a02","mcp_get_code":{"code_sha256":"cfc5de3ebf0a6a02"}},{"arxiv_id":"2104.02768","paper":"/paper/robust-semantic-interpretability-revisiting","title":"Robust Semantic Interpretability: Revisiting Concept Activation Vectors","date":"2021-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"keiserlab/rcav","path":"rcav_utils.py","file_url":"https://github.com/keiserlab/rcav/blob/HEAD/rcav_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"97795a7b853c4260","mcp_get_code":{"code_sha256":"97795a7b853c4260"}},{"arxiv_id":"2009.10588","paper":"/paper/anomalous-diffusion-dynamics-of-learning-in","title":"Anomalous diffusion dynamics of learning in deep neural networks","date":"2020-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ifgovh/Anomalous-diffusion-dynamics-of-SGD","path":"get_gradient_weight/gradient_noise.py","file_url":"https://github.com/ifgovh/Anomalous-diffusion-dynamics-of-SGD/blob/HEAD/get_gradient_weight/gradient_noise.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"81aabb5979983838","mcp_get_code":{"code_sha256":"81aabb5979983838"}},{"arxiv_id":"2006.09313","paper":"/paper/hausdorff-dimension-stochastic-differential","title":"Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks","date":"2020-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"umutsimsekli/Hausdorff-Dimension-and-Generalization","path":"src/utils.py","file_url":"https://github.com/umutsimsekli/Hausdorff-Dimension-and-Generalization/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"81aabb5979983838","mcp_get_code":{"code_sha256":"81aabb5979983838"}},{"arxiv_id":"2002.08709","paper":"/paper/do-we-need-zero-training-loss-after-achieving","title":"Do We Need Zero Training Loss After Achieving Zero Training Error?","date":"2020-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"takashiishida/flooding","path":"utils/utils_algo.py","file_url":"https://github.com/takashiishida/flooding/blob/HEAD/utils/utils_algo.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"d251cab596a514b3","mcp_get_code":{"code_sha256":"d251cab596a514b3"}},{"arxiv_id":"1703.09452","paper":"/paper/segan-speech-enhancement-generative","title":"SEGAN: Speech Enhancement Generative Adversarial Network","date":"2017-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"santi-pdp/segan_pytorch","path":"segan/models/ops.py","file_url":"https://github.com/santi-pdp/segan_pytorch/blob/HEAD/segan/models/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"26c162c3caa1223a","mcp_get_code":{"code_sha256":"26c162c3caa1223a"}},{"arxiv_id":"1604.06646","paper":"/paper/synthetic-data-for-text-localisation-in","title":"Synthetic Data for Text Localisation in Natural Images","date":"2016-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ankush-me/SynthText","path":"poisson_reconstruct.py","file_url":"https://github.com/ankush-me/SynthText/blob/HEAD/poisson_reconstruct.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":"15c813055f58cb66","mcp_get_code":{"code_sha256":"15c813055f58cb66"}},{"arxiv_id":"1504.06852","paper":"/paper/flownet-learning-optical-flow-with","title":"FlowNet: Learning Optical Flow with Convolutional Networks","date":"2015-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ClementPinard/Pytorch-Correlation-extension","path":"check.py","file_url":"https://github.com/ClementPinard/Pytorch-Correlation-extension/blob/HEAD/check.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"19ee68e6a7f1011b","mcp_get_code":{"code_sha256":"19ee68e6a7f1011b"}}]}