{"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/update","entry":"update","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":31,"n_papers_ran":10,"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":32,"n_samples_ran":9,"n_samples_fingerprinted":3,"n_places":36,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":4,"ran_fixture":1,"ran":3,"unverified":23},"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":"2503.16247","paper":"/paper/openmibood-open-medical-imaging-benchmarks","title":"OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection","date":"2025-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"cabce999068a95b5","mcp_get_code":{"code_sha256":"cabce999068a95b5"}},{"arxiv_id":"2410.13831","paper":"/paper/the-disparate-benefits-of-deep-ensembles","title":"The Disparate Benefits of Deep Ensembles","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-jku/disparate-benefits","path":"source/utils/train_utils.py","file_url":"https://github.com/ml-jku/disparate-benefits/blob/HEAD/source/utils/train_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c40d52fe7476ce1","mcp_get_code":{"code_sha256":"9c40d52fe7476ce1"}},{"arxiv_id":"2408.09153","paper":"/paper/are-clip-features-all-you-need-for-universal","title":"Are CLIP features all you need for Universal Synthetic Image Origin Attribution?","date":"2024-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ciodar/universalattribution","path":"utils/config.py","file_url":"https://github.com/ciodar/universalattribution/blob/HEAD/utils/config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3d5ab682980fa9db","mcp_get_code":{"code_sha256":"3d5ab682980fa9db"}},{"arxiv_id":"2404.03502","paper":"/paper/ai-and-the-problem-of-knowledge-collapse","title":"AI and the Problem of Knowledge Collapse","date":"2024-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aristotle-tek/knowledge-collapse","path":"simulation/gen_plots.py","file_url":"https://github.com/aristotle-tek/knowledge-collapse/blob/HEAD/simulation/gen_plots.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e5fc876a359dfd4","mcp_get_code":{"code_sha256":"3e5fc876a359dfd4"}},{"arxiv_id":"2402.13147","paper":"/paper/subiq-inverse-soft-q-learning-for-offline","title":"SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning","date":"2024-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmhuy0/SPRINQL","path":"Sources/algos/sprinql.py","file_url":"https://github.com/hmhuy0/SPRINQL/blob/HEAD/Sources/algos/sprinql.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac2a3a2383d7e234","mcp_get_code":{"code_sha256":"ac2a3a2383d7e234"}},{"arxiv_id":"2402.05137","paper":"/paper/ltu-ili-an-all-in-one-framework-for-implicit","title":"LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maho3/ltu-ili","path":"ili/utils/import_utils.py","file_url":"https://github.com/maho3/ltu-ili/blob/HEAD/ili/utils/import_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"594fa6e8b822a6b5","mcp_get_code":{"code_sha256":"594fa6e8b822a6b5"}},{"arxiv_id":"2311.01479","paper":"/paper/detecting-out-of-distribution-through-the","title":"Detecting Out-of-Distribution Through the Lens of Neural Collapse","date":"2023-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"litianliu/nci-ood","path":"scripts/eval_ood.py","file_url":"https://github.com/litianliu/nci-ood/blob/HEAD/scripts/eval_ood.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cabce999068a95b5","mcp_get_code":{"code_sha256":"cabce999068a95b5"}},{"arxiv_id":"2310.17966","paper":"/paper/train-once-get-a-family-state-adaptive-1","title":"Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning","date":"2023-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/famo2o","path":"jax_iql/family_learner.py","file_url":"https://github.com/leaplabthu/famo2o/blob/HEAD/jax_iql/family_learner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9645d2497b243871","mcp_get_code":{"code_sha256":"9645d2497b243871"}},{"arxiv_id":"2306.09623","paper":"/paper/from-hypergraph-energy-functions-to","title":"From Hypergraph Energy Functions to Hypergraph Neural Networks","date":"2023-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"malllabiisc/HyperGCN","path":"model/utils.py","file_url":"https://github.com/malllabiisc/HyperGCN/blob/HEAD/model/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":"4ec8917a615ffe2d","mcp_get_code":{"code_sha256":"4ec8917a615ffe2d"}},{"arxiv_id":"2210.02330","paper":"/paper/revisiting-graph-contrastive-learning-from","title":"Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum","date":"2022-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liun-online/SpCo","path":"dgi_spco/execute.py","file_url":"https://github.com/liun-online/SpCo/blob/HEAD/dgi_spco/execute.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cd66e0fa4b2e9129","mcp_get_code":{"code_sha256":"cd66e0fa4b2e9129"}},{"arxiv_id":"2210.00912","paper":"/paper/federated-domain-generalization-for-image","title":"Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer","date":"2022-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JeremyCJM/CCST","path":"utils/rsc_utils_densenet.py","file_url":"https://github.com/JeremyCJM/CCST/blob/HEAD/utils/rsc_utils_densenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"69f7b99ec0fda254","mcp_get_code":{"code_sha256":"69f7b99ec0fda254"}},{"arxiv_id":"2208.01462","paper":"/paper/physics-informed-deep-super-resolution-for","title":"Physics-informed Deep Super-resolution for Spatiotemporal Data","date":"2022-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"paulpuren/physr","path":"Dataset/2DGS_FD_Solver[RK4].py","file_url":"https://github.com/paulpuren/physr/blob/HEAD/Dataset/2DGS_FD_Solver%5BRK4%5D.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f8a80544a3264dab","mcp_get_code":{"code_sha256":"f8a80544a3264dab"}},{"arxiv_id":"2208.01462","paper":"/paper/physics-informed-deep-super-resolution-for","title":"Physics-informed Deep Super-resolution for Spatiotemporal Data","date":"2022-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"paulpuren/physr","path":"Dataset/3DGS_FD_Solver[RK4].py","file_url":"https://github.com/paulpuren/physr/blob/HEAD/Dataset/3DGS_FD_Solver%5BRK4%5D.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dab0180fa32337d1","mcp_get_code":{"code_sha256":"dab0180fa32337d1"}},{"arxiv_id":"2207.06680","paper":"/paper/equivariant-hypergraph-diffusion-neural","title":"Equivariant Hypergraph Diffusion Neural Operators","date":"2022-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"graph-com/ed-hnn","path":"models/hypergcn.py","file_url":"https://github.com/graph-com/ed-hnn/blob/HEAD/models/hypergcn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ec8917a615ffe2d","mcp_get_code":{"code_sha256":"4ec8917a615ffe2d"}},{"arxiv_id":"2204.12386","paper":"/paper/learning-meta-word-embeddings-by-unsupervised-1","title":"Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings","date":"2022-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LivNLP/meta-concat","path":"GlobalME.py","file_url":"https://github.com/LivNLP/meta-concat/blob/HEAD/GlobalME.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b6fdd29297f200ae","mcp_get_code":{"code_sha256":"b6fdd29297f200ae"}},{"arxiv_id":"2204.05306","paper":"/paper/full-spectrum-out-of-distribution-detection","title":"Full-Spectrum Out-of-Distribution Detection","date":"2022-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"cabce999068a95b5","mcp_get_code":{"code_sha256":"cabce999068a95b5"}},{"arxiv_id":"2110.01528","paper":"/paper/large-batch-experience-replay","title":"Large Batch Experience Replay","date":"2021-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xavierchanglingli/regularized-optimal-experience-replay","path":"sac_learner.py","file_url":"https://github.com/xavierchanglingli/regularized-optimal-experience-replay/blob/HEAD/sac_learner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33815b2ad6fcd3d0","mcp_get_code":{"code_sha256":"33815b2ad6fcd3d0"}},{"arxiv_id":"2108.04763","paper":"/paper/imitation-learning-by-reinforcement-learning","title":"Imitation Learning by Reinforcement Learning","date":"2021-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"spotify-research/il-by-rl","path":"d4rl_train.py","file_url":"https://github.com/spotify-research/il-by-rl/blob/HEAD/d4rl_train.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":"c311e1212eabc873","mcp_get_code":{"code_sha256":"c311e1212eabc873"}},{"arxiv_id":"2108.04763","paper":"/paper/imitation-learning-by-reinforcement-learning","title":"Imitation Learning by Reinforcement Learning","date":"2021-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"spotify-research/il-by-rl","path":"d4rl_train_sqil.py","file_url":"https://github.com/spotify-research/il-by-rl/blob/HEAD/d4rl_train_sqil.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":"52dbc718a25a63ce","mcp_get_code":{"code_sha256":"52dbc718a25a63ce"}},{"arxiv_id":"2105.01992","paper":"/paper/legoeval-an-open-source-toolkit-for-dialogue","title":"LEGOEval: An Open-Source Toolkit for Dialogue System Evaluation via Crowdsourcing","date":"2021-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yooli23/LEGOEval","path":"app/main_loop.py","file_url":"https://github.com/yooli23/LEGOEval/blob/HEAD/app/main_loop.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":"ac82dbca2ac74a9e","mcp_get_code":{"code_sha256":"ac82dbca2ac74a9e"}},{"arxiv_id":"2105.00956","paper":"/paper/unignn-a-unified-framework-for-graph-and","title":"UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks","date":"2021-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OneForward/UniGNN","path":"model/HyperGCN.py","file_url":"https://github.com/OneForward/UniGNN/blob/HEAD/model/HyperGCN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ec8917a615ffe2d","mcp_get_code":{"code_sha256":"4ec8917a615ffe2d"}},{"arxiv_id":"2005.01917","paper":"/paper/learning-selection-strategies-in-buchberger-s","title":"Learning selection strategies in Buchberger's algorithm","date":"2020-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dylanpeifer/deepgroebner","path":"deepgroebner/buchberger.py","file_url":"https://github.com/dylanpeifer/deepgroebner/blob/HEAD/deepgroebner/buchberger.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"19d1c740f3096540","mcp_get_code":{"code_sha256":"19d1c740f3096540"}},{"arxiv_id":"1906.01549","paper":"/paper/streaming-variational-monte-carlo","title":"Streaming Variational Monte Carlo","date":"2019-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"catniplab/svmc","path":"svmc/gp.py","file_url":"https://github.com/catniplab/svmc/blob/HEAD/svmc/gp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"31e15502151f7bc6","mcp_get_code":{"code_sha256":"31e15502151f7bc6"}},{"arxiv_id":"1902.02384","paper":"/paper/global-explanations-of-neural-networks","title":"Global Explanations of Neural Networks: Mapping the Landscape of Predictions","date":"2019-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"capitalone/global-attribution-mapping","path":"gam/clustering.py","file_url":"https://github.com/capitalone/global-attribution-mapping/blob/HEAD/gam/clustering.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":"1d998fd0012807db","mcp_get_code":{"code_sha256":"1d998fd0012807db"}},{"arxiv_id":"1812.03982","paper":"/paper/slowfast-networks-for-video-recognition","title":"SlowFast Networks for Video Recognition","date":"2018-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tonysy/PyAction","path":"pyaction/configs/base_config.py","file_url":"https://github.com/tonysy/PyAction/blob/HEAD/pyaction/configs/base_config.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":"7f9eb5a8ffa36af9","mcp_get_code":{"code_sha256":"7f9eb5a8ffa36af9"}},{"arxiv_id":"1810.03993","paper":"/paper/model-cards-for-model-reporting","title":"Model Cards for Model Reporting","date":"2018-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tensorflow/model-card-toolkit","path":"model_card_toolkit/utils/json_utils.py","file_url":"https://github.com/tensorflow/model-card-toolkit/blob/HEAD/model_card_toolkit/utils/json_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":"201268fcbea40035","mcp_get_code":{"code_sha256":"201268fcbea40035"}},{"arxiv_id":"1707.06347","paper":"/paper/proximal-policy-optimization-algorithms","title":"Proximal Policy Optimization Algorithms","date":"2017-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gouet/Acrobot-PPO","path":"ppo.py","file_url":"https://github.com/Gouet/Acrobot-PPO/blob/HEAD/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7271dace2360dd93","mcp_get_code":{"code_sha256":"7271dace2360dd93"}},{"arxiv_id":"1707.06347","paper":"/paper/proximal-policy-optimization-algorithms","title":"Proximal Policy Optimization Algorithms","date":"2017-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gouet/PPO-pytorch","path":"ppo.py","file_url":"https://github.com/Gouet/PPO-pytorch/blob/HEAD/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e7bdf63d50d132e","mcp_get_code":{"code_sha256":"3e7bdf63d50d132e"}},{"arxiv_id":"1707.06347","paper":"/paper/proximal-policy-optimization-algorithms","title":"Proximal Policy Optimization Algorithms","date":"2017-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gouet/PPO-gym","path":"ppo.py","file_url":"https://github.com/Gouet/PPO-gym/blob/HEAD/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0b170016a5f01a6e","mcp_get_code":{"code_sha256":"0b170016a5f01a6e"}},{"arxiv_id":"1705.08039","paper":"/paper/poincare-embeddings-for-learning-hierarchical","title":"Poincaré Embeddings for Learning Hierarchical Representations","date":"2017-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nishnik/poincare_embeddings","path":"poincare.py","file_url":"https://github.com/nishnik/poincare_embeddings/blob/HEAD/poincare.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b61dce6ea5a27ad7","mcp_get_code":{"code_sha256":"b61dce6ea5a27ad7"}},{"arxiv_id":"1705.08039","paper":"/paper/poincare-embeddings-for-learning-hierarchical","title":"Poincaré Embeddings for Learning Hierarchical Representations","date":"2017-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nishnik/poincare_embeddings","path":"poincare_adam.py","file_url":"https://github.com/nishnik/poincare_embeddings/blob/HEAD/poincare_adam.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4a4bb7cf81fded2c","mcp_get_code":{"code_sha256":"4a4bb7cf81fded2c"}},{"arxiv_id":"1312.6114","paper":"/paper/auto-encoding-variational-bayes","title":"Auto-Encoding Variational Bayes","date":"2013-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhihanyang2022/aevb-tutorial","path":"05_vrnn/matrix_evolve_animate_subplots.py","file_url":"https://github.com/zhihanyang2022/aevb-tutorial/blob/HEAD/05_vrnn/matrix_evolve_animate_subplots.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3b0c8e0b6837b18e","mcp_get_code":{"code_sha256":"3b0c8e0b6837b18e"}},{"arxiv_id":"Seidenschwarz_Simple_Cues_Lead_to_a_Strong_Multi-Object_Tracker_CVPR_2023_paper","paper":null,"title":"arXiv:Seidenschwarz_Simple_Cues_Lead_to_a_Strong_Multi-Object_Tracker_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"dvl-tum/GHOST","path":"src/utils.py","file_url":"https://github.com/dvl-tum/GHOST/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":"e72e4b2b50b2aa41","mcp_get_code":{"code_sha256":"e72e4b2b50b2aa41"}},{"arxiv_id":"2025.findings-emnlp.484","paper":null,"title":"arXiv:2025.findings-emnlp.484","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"UbiquitousLearning/DroidCall","path":"recorder/client.py","file_url":"https://github.com/UbiquitousLearning/DroidCall/blob/HEAD/recorder/client.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e6915b8651d6055","mcp_get_code":{"code_sha256":"8e6915b8651d6055"}},{"arxiv_id":"2021.findings-emnlp.130","paper":null,"title":"arXiv:2021.findings-emnlp.130","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"nutcrtnk/DHGNet","path":"src/run_setting.py","file_url":"https://github.com/nutcrtnk/DHGNet/blob/HEAD/src/run_setting.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbf00f8defe72c9d","mcp_get_code":{"code_sha256":"bbf00f8defe72c9d"}},{"arxiv_id":"136820244","paper":null,"title":"arXiv:136820244","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Sirui-Xu/STARS","path":"deterministic/utils/h36_3d_viz.py","file_url":"https://github.com/Sirui-Xu/STARS/blob/HEAD/deterministic/utils/h36_3d_viz.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"98c8a875f6a616cf","mcp_get_code":{"code_sha256":"98c8a875f6a616cf"}}]}