{"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/forward-backward","entry":"forward_backward","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":5,"n_papers_ran":1,"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":5,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"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":"2406.08938","paper":"/paper/mirror-and-preconditioned-gradient-descent-in","title":"Mirror and Preconditioned Gradient Descent in Wasserstein Space","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clbonet/Mirror_and_Preconditioned_Gradient_Descent_in_Wasserstein_Space","path":"lib_jax/mirror_gaussians.py","file_url":"https://github.com/clbonet/Mirror_and_Preconditioned_Gradient_Descent_in_Wasserstein_Space/blob/HEAD/lib_jax/mirror_gaussians.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"becb8047c46a5257","mcp_get_code":{"code_sha256":"becb8047c46a5257"}},{"arxiv_id":"2401.12973","paper":"/paper/in-context-language-learning-arhitectures-and","title":"In-Context Language Learning: Architectures and Algorithms","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"berlino/seq_icl","path":"batched_baum_welch.py","file_url":"https://github.com/berlino/seq_icl/blob/HEAD/batched_baum_welch.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":"7d16fa77eed96638","mcp_get_code":{"code_sha256":"7d16fa77eed96638"}},{"arxiv_id":"2012.14952","paper":"/paper/bayesian-hmm-clustering-of-x-vector-sequences","title":"Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks","date":null,"month_inferred_from_arxiv_id":"2020-12","title_source":"archive","repo":"BUTSpeechFIT/VBx","path":"VBx/VBx.py","file_url":"https://github.com/BUTSpeechFIT/VBx/blob/HEAD/VBx/VBx.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"259e6f64ed883164","mcp_get_code":{"code_sha256":"259e6f64ed883164"}},{"arxiv_id":"1711.10925","paper":"/paper/deep-image-prior","title":"Deep Image Prior","date":"2017-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dniku/perceptual-gradient-networks","path":"train_pgn.py","file_url":"https://github.com/dniku/perceptual-gradient-networks/blob/HEAD/train_pgn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MPL-2.0","inline_ok":false,"code_sha256_prefix":"5be41c66fa843db4","mcp_get_code":{"code_sha256":"5be41c66fa843db4"}},{"arxiv_id":"Feng_Optimal_Gradient_Checkpoint_Search_for_Arbitrary_Computation_Graphs_CVPR_2021_paper","paper":null,"title":"arXiv:Feng_Optimal_Gradient_Checkpoint_Search_for_Arbitrary_Computation_Graphs_CVPR_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lordfjw/OptimalGradCheckpointing","path":"benchmark.py","file_url":"https://github.com/lordfjw/OptimalGradCheckpointing/blob/HEAD/benchmark.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1b8472ebfaaeee99","mcp_get_code":{"code_sha256":"1b8472ebfaaeee99"}}]}