{"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/sigmoid-cross-entropy","entry":"sigmoid_cross_entropy","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-25T09:33:49+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":6,"n_papers_ran":2,"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":6,"n_samples_ran":2,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"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":"2403.20126","paper":"/paper/eclipse-efficient-continual-learning-in","title":"ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clovaai/ECLIPSE","path":"continual/method_wrapper/loss.py","file_url":"https://github.com/clovaai/ECLIPSE/blob/HEAD/continual/method_wrapper/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"894427132bec06c1","mcp_get_code":{"code_sha256":"894427132bec06c1"}},{"arxiv_id":"2402.04845","paper":"/paper/alphafold-meets-flow-matching-for-generating","title":"AlphaFold Meets Flow Matching for Generating Protein Ensembles","date":"2024-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bjing2016/alphaflow","path":"alphaflow/utils/loss.py","file_url":"https://github.com/bjing2016/alphaflow/blob/HEAD/alphaflow/utils/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f426d17982cad0b9","mcp_get_code":{"code_sha256":"f426d17982cad0b9"}},{"arxiv_id":"2311.01196","paper":"/paper/combating-bilateral-edge-noise-for-robust-1","title":"Combating Bilateral Edge Noise for Robust Link Prediction","date":"2023-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"flyingdoog/PTDNet","path":"metrics.py","file_url":"https://github.com/flyingdoog/PTDNet/blob/HEAD/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a2f118a9c3aab3ec","mcp_get_code":{"code_sha256":"a2f118a9c3aab3ec"}},{"arxiv_id":"2306.03117","paper":"/paper/score-based-enhanced-sampling-for-protein","title":"Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling","date":"2023-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lujiarui/Str2Str","path":"src/models/loss.py","file_url":"https://github.com/lujiarui/Str2Str/blob/HEAD/src/models/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"15326c79adab79b0","mcp_get_code":{"code_sha256":"15326c79adab79b0"}},{"arxiv_id":"1905.09432","paper":"/paper/learning-discrete-and-continuous-factors-of","title":"Learning Discrete and Continuous Factors of Data via Alternating Disentanglement","date":"2019-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snu-mllab/DisentanglementICML19","path":"tfops/loss.py","file_url":"https://github.com/snu-mllab/DisentanglementICML19/blob/HEAD/tfops/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"089b6e92ad61625f","mcp_get_code":{"code_sha256":"089b6e92ad61625f"}},{"arxiv_id":"1703.04046","paper":"/paper/deepsleepnet-a-model-for-automatic-sleep","title":"DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG","date":"2017-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akaraspt/deepsleepnet","path":"tensorlayer/cost.py","file_url":"https://github.com/akaraspt/deepsleepnet/blob/HEAD/tensorlayer/cost.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":"77743670f997e3ab","mcp_get_code":{"code_sha256":"77743670f997e3ab"}}]}