{"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/binary-accuracy","entry":"binary_accuracy","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":13,"n_papers_ran":8,"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":12,"n_samples_ran":6,"n_samples_fingerprinted":4,"n_places":14,"n_places_pointer_only":5,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":1,"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":"2608.15601","paper":"/paper/arxiv-2608-15601","title":"Quantum Models with Multi-Stage Training for Compositional Concept Generalization","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"Mina-Abbaszade/Quantum-Multimodality","path":"src/common/binary_metrics.py","file_url":"https://github.com/Mina-Abbaszade/Quantum-Multimodality/blob/HEAD/src/common/binary_metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"93b7a9ad79da59d7","mcp_get_code":{"code_sha256":"93b7a9ad79da59d7"}},{"arxiv_id":"2505.05195","paper":"/paper/concept-based-unsupervised-domain-adaptation","title":"Concept-Based Unsupervised Domain Adaptation","date":"2025-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xmed-lab/CUDA","path":"analysis/a_distance.py","file_url":"https://github.com/xmed-lab/CUDA/blob/HEAD/analysis/a_distance.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c9d98412ada0b6a","mcp_get_code":{"code_sha256":"2c9d98412ada0b6a"}},{"arxiv_id":"2406.04138","paper":"/paper/the-3d-pc-a-benchmark-for-visual-perspective","title":"The 3D-PC: a benchmark for visual perspective taking in humans and machines","date":"2024-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"serre-lab/VPT","path":"src/utils.py","file_url":"https://github.com/serre-lab/VPT/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC-BY-4.0","inline_ok":false,"code_sha256_prefix":"22032d841d534525","mcp_get_code":{"code_sha256":"22032d841d534525"}},{"arxiv_id":"2405.16391","paper":"/paper/when-does-compositional-structure-yield","title":"When does compositional structure yield compositional generalization? A kernel theory","date":"2024-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sflippl/compositional-generalization","path":"simple_compositional_tasks/models/kernel_machines.py","file_url":"https://github.com/sflippl/compositional-generalization/blob/HEAD/simple_compositional_tasks/models/kernel_machines.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a8f89e3acd77db3a","mcp_get_code":{"code_sha256":"a8f89e3acd77db3a"}},{"arxiv_id":"2310.16318","paper":"/paper/modality-agnostic-self-supervised-learning","title":"Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder","date":"2023-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alinlab/MetaMAE","path":"linear_evaluation.py","file_url":"https://github.com/alinlab/MetaMAE/blob/HEAD/linear_evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d98aa6b239eec82a","mcp_get_code":{"code_sha256":"d98aa6b239eec82a"}},{"arxiv_id":"2108.10252","paper":"/paper/federated-multi-task-learning-under-a-mixture","title":"Federated Multi-Task Learning under a Mixture of Distributions","date":"2021-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"omarfoq/FedEM","path":"utils/metrics.py","file_url":"https://github.com/omarfoq/FedEM/blob/HEAD/utils/metrics.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":"d86b1b7917839f9d","mcp_get_code":{"code_sha256":"d86b1b7917839f9d"}},{"arxiv_id":"2103.05487","paper":"/paper/unicornn-a-recurrent-model-for-learning-very","title":"UnICORNN: A recurrent model for learning very long time dependencies","date":"2021-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tk-rusch/unicornn","path":"IMDB/IMDB_task.py","file_url":"https://github.com/tk-rusch/unicornn/blob/HEAD/IMDB/IMDB_task.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fd308c50fdd7ff27","mcp_get_code":{"code_sha256":"fd308c50fdd7ff27"}},{"arxiv_id":"2010.00951","paper":"/paper/coupled-oscillatory-recurrent-neural-network","title":"Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies","date":"2020-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tk-rusch/coRNN","path":"HAR-2/har2_task.py","file_url":"https://github.com/tk-rusch/coRNN/blob/HEAD/HAR-2/har2_task.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd308c50fdd7ff27","mcp_get_code":{"code_sha256":"fd308c50fdd7ff27"}},{"arxiv_id":"2010.00951","paper":"/paper/coupled-oscillatory-recurrent-neural-network","title":"Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies","date":"2020-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tk-rusch/coRNN","path":"IMDB/IMDB_task.py","file_url":"https://github.com/tk-rusch/coRNN/blob/HEAD/IMDB/IMDB_task.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"10c7b6d241358ca5","mcp_get_code":{"code_sha256":"10c7b6d241358ca5"}},{"arxiv_id":"1907.00865","paper":"/paper/radial-bayesian-neural-networks-robust","title":"Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning","date":"2019-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SebFar/radial_bnn","path":"model/metric.py","file_url":"https://github.com/SebFar/radial_bnn/blob/HEAD/model/metric.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd559b979213c51f","mcp_get_code":{"code_sha256":"fd559b979213c51f"}},{"arxiv_id":"1905.05583","paper":"/paper/how-to-fine-tune-bert-for-text-classification","title":"How to Fine-Tune BERT for Text Classification?","date":"2019-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ongunuzaymacar/comparatively-finetuning-bert","path":"utils/model_utils.py","file_url":"https://github.com/ongunuzaymacar/comparatively-finetuning-bert/blob/HEAD/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"195884f8c99c2ded","mcp_get_code":{"code_sha256":"195884f8c99c2ded"}},{"arxiv_id":"1409.7495","paper":"/paper/unsupervised-domain-adaptation-by","title":"Unsupervised Domain Adaptation by Backpropagation","date":"2014-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuml/Transfer-Learning-Library","path":"tllib/alignment/dann.py","file_url":"https://github.com/thuml/Transfer-Learning-Library/blob/HEAD/tllib/alignment/dann.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c9d98412ada0b6a","mcp_get_code":{"code_sha256":"2c9d98412ada0b6a"}},{"arxiv_id":"1408.5882","paper":"/paper/convolutional-neural-networks-for-sentence","title":"Convolutional Neural Networks for Sentence Classification","date":"2014-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"piyush2896/CNN-Text-Classifier","path":"nn/metrics.py","file_url":"https://github.com/piyush2896/CNN-Text-Classifier/blob/HEAD/nn/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d51abac955ff517c","mcp_get_code":{"code_sha256":"d51abac955ff517c"}},{"arxiv_id":"2022.findings-emnlp.47","paper":null,"title":"arXiv:2022.findings-emnlp.47","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lancopku/DAN","path":"functions.py","file_url":"https://github.com/lancopku/DAN/blob/HEAD/functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d57e3a6e0a6abc62","mcp_get_code":{"code_sha256":"d57e3a6e0a6abc62"}}]}