{"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/compute-losses","entry":"compute_losses","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":3,"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":10,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":10,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"unverified":6},"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":"2506.13150","paper":"/paper/federated-admm-from-bayesian-duality","title":"Federated ADMM from Bayesian Duality","date":"2025-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"team-approx-bayes/bayes-admm","path":"toyexamples/main_onestep.py","file_url":"https://github.com/team-approx-bayes/bayes-admm/blob/HEAD/toyexamples/main_onestep.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"e02f349e31f703f9","mcp_get_code":{"code_sha256":"e02f349e31f703f9"}},{"arxiv_id":"2504.02199","paper":"/paper/esc-erasing-space-concept-for-knowledge","title":"ESC: Erasing Space Concept for Knowledge Deletion","date":"2025-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KU-VGI/ESC","path":"unlearn.py","file_url":"https://github.com/KU-VGI/ESC/blob/HEAD/unlearn.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"232f710ba2a3df96","mcp_get_code":{"code_sha256":"232f710ba2a3df96"}},{"arxiv_id":"2409.14747","paper":"/paper/distribution-level-feature-distancing-for","title":"Distribution-Level Feature Distancing for Machine Unlearning: Towards a Better Trade-off Between Model Utility and Forgetting","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Dasol-Choi/DLFD","path":"evaluation/mia.py","file_url":"https://github.com/Dasol-Choi/DLFD/blob/HEAD/evaluation/mia.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":"7e66e3d5c261f3c2","mcp_get_code":{"code_sha256":"7e66e3d5c261f3c2"}},{"arxiv_id":"2409.09811","paper":"/paper/prose-fd-a-multimodal-pde-foundation-model","title":"PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics","date":"2024-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"felix-lyx/prose","path":"prose_ode/evaluate.py","file_url":"https://github.com/felix-lyx/prose/blob/HEAD/prose_ode/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a6d1320deafb7ee4","mcp_get_code":{"code_sha256":"a6d1320deafb7ee4"}},{"arxiv_id":"2409.09811","paper":"/paper/prose-fd-a-multimodal-pde-foundation-model","title":"PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics","date":"2024-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"felix-lyx/prose","path":"prose_pde/evaluate.py","file_url":"https://github.com/felix-lyx/prose/blob/HEAD/prose_pde/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e5d31e1a2268e3f","mcp_get_code":{"code_sha256":"7e5d31e1a2268e3f"}},{"arxiv_id":"2310.17359","paper":"/paper/se-3-diffusion-model-based-point-cloud","title":"SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jiang-HB/DiffusionReg","path":"utils/losses.py","file_url":"https://github.com/Jiang-HB/DiffusionReg/blob/HEAD/utils/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9678a2e24b9ac1cb","mcp_get_code":{"code_sha256":"9678a2e24b9ac1cb"}},{"arxiv_id":"2110.00641","paper":"/paper/batch-size-invariance-for-policy-optimization","title":"Batch size-invariance for policy optimization","date":"2021-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openai/ppo-ewma","path":"ppo_ewma/ppo.py","file_url":"https://github.com/openai/ppo-ewma/blob/HEAD/ppo_ewma/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6532fc4d2c589522","mcp_get_code":{"code_sha256":"6532fc4d2c589522"}},{"arxiv_id":"2009.04416","paper":"/paper/phasic-policy-gradient","title":"Phasic Policy Gradient","date":"2020-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openai/phasic-policy-gradient","path":"phasic_policy_gradient/ppo.py","file_url":"https://github.com/openai/phasic-policy-gradient/blob/HEAD/phasic_policy_gradient/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d9e098f75a09f80","mcp_get_code":{"code_sha256":"6d9e098f75a09f80"}},{"arxiv_id":"1901.02970","paper":"/paper/normalized-object-coordinate-space-for","title":"Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation","date":"2019-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sahithchada/NOCS_PyTorch","path":"loss.py","file_url":"https://github.com/sahithchada/NOCS_PyTorch/blob/HEAD/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b73499f3de89c7b5","mcp_get_code":{"code_sha256":"b73499f3de89c7b5"}},{"arxiv_id":"aaai_35277","paper":null,"title":"arXiv:aaai_35277","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"vlgiitr/LoRA-Unlearn","path":"resnet/evaluate.py","file_url":"https://github.com/vlgiitr/LoRA-Unlearn/blob/HEAD/resnet/evaluate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d75d39b1a2c049fa","mcp_get_code":{"code_sha256":"d75d39b1a2c049fa"}}]}