{"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/batch-jacobian","entry":"batch_jacobian","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":9,"n_papers_ran":4,"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":3,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":1,"ran":0,"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":"2511.22128","paper":"/paper/arxiv-2511-22128","title":"A Variational Manifold Embedding Framework for Nonlinear Dimensionality Reduction","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"john-vastola/varembed-neurreps25","path":"functions/embedding_functions.py","file_url":"https://github.com/john-vastola/varembed-neurreps25/blob/HEAD/functions/embedding_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"44701842bb9117cd","mcp_get_code":{"code_sha256":"44701842bb9117cd"}},{"arxiv_id":"2402.08090","paper":"/paper/learning-neural-contracting-dynamics-extended","title":"Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees","date":"2024-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seanjaffe1/extended-linearized-contracting-dynamics","path":"models/elcd.py","file_url":"https://github.com/seanjaffe1/extended-linearized-contracting-dynamics/blob/HEAD/models/elcd.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":"0238ff2b3f78a2ad","mcp_get_code":{"code_sha256":"0238ff2b3f78a2ad"}},{"arxiv_id":"2306.04675","paper":"/paper/exposing-flaws-of-generative-model-evaluation-1","title":"Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models","date":"2023-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rtqichen/residual-flows","path":"resflows/layers/iresblock.py","file_url":"https://github.com/rtqichen/residual-flows/blob/HEAD/resflows/layers/iresblock.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":"85a4b5aac95f359d","mcp_get_code":{"code_sha256":"85a4b5aac95f359d"}},{"arxiv_id":"2305.18965","paper":"/paper/node-embedding-from-neural-hamiltonian-orbits","title":"Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks","date":"2023-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zknus/hamiltonian-gnn","path":"layers/H_1.py","file_url":"https://github.com/zknus/hamiltonian-gnn/blob/HEAD/layers/H_1.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d2a165a621028d4","mcp_get_code":{"code_sha256":"6d2a165a621028d4"}},{"arxiv_id":"2304.06094","paper":"/paper/energy-guided-entropic-neural-optimal","title":"Energy-guided Entropic Neural Optimal Transport","date":"2023-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"petrmokrov/energy-guided-entropic-ot","path":"src/utils.py","file_url":"https://github.com/petrmokrov/energy-guided-entropic-ot/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":"e6d9dfda0f695bd3","mcp_get_code":{"code_sha256":"e6d9dfda0f695bd3"}},{"arxiv_id":"2206.02416","paper":"/paper/embrace-the-gap-vaes-perform-independent","title":"Embrace the Gap: VAEs Perform Independent Mechanism Analysis","date":"2022-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rpatrik96/ima-vae","path":"ima_vae/utils.py","file_url":"https://github.com/rpatrik96/ima-vae/blob/HEAD/ima_vae/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":"ebde3b734314d2fc","mcp_get_code":{"code_sha256":"ebde3b734314d2fc"}},{"arxiv_id":"2112.14195","paper":"/paper/exponential-family-model-based-reinforcement","title":"Exponential Family Model-Based Reinforcement Learning via Score Matching","date":"2021-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anmolkabra/score-matching-rl","path":"utils.py","file_url":"https://github.com/anmolkabra/score-matching-rl/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2e28c4d3306dabfc","mcp_get_code":{"code_sha256":"2e28c4d3306dabfc"}},{"arxiv_id":"1906.06307","paper":"/paper/a-signal-propagation-perspective-for-pruning","title":"A Signal Propagation Perspective for Pruning Neural Networks at Initialization","date":"2019-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"namhoonlee/spp-public","path":"spp/model.py","file_url":"https://github.com/namhoonlee/spp-public/blob/HEAD/spp/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c621a9782d26e315","mcp_get_code":{"code_sha256":"c621a9782d26e315"}},{"arxiv_id":"1811.00995","paper":"/paper/invertible-residual-networks","title":"Invertible Residual Networks","date":"2018-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yperugachidiaz/invertible_densenets","path":"lib/layers/iresblock.py","file_url":"https://github.com/yperugachidiaz/invertible_densenets/blob/HEAD/lib/layers/iresblock.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":"85a4b5aac95f359d","mcp_get_code":{"code_sha256":"85a4b5aac95f359d"}}]}