{"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/rnn","entry":"RNN","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":11,"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":13,"n_samples_ran":11,"n_samples_fingerprinted":1,"n_places":13,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":11,"unverified":2},"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":"2409.19212","paper":"/paper/an-accelerated-algorithm-for-stochastic","title":"An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness","date":"2024-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingruiliu-ml-lab/accelerated-bilevel-optimization-unbounded-smoothness","path":"auc_maximization/methods/accbo.py","file_url":"https://github.com/mingruiliu-ml-lab/accelerated-bilevel-optimization-unbounded-smoothness/blob/HEAD/auc_maximization/methods/accbo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"71942a381042e08c","mcp_get_code":{"code_sha256":"71942a381042e08c"}},{"arxiv_id":"2401.09587","paper":"/paper/bilevel-optimization-under-unbounded","title":"Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingruiliu-ml-lab/bilevel-optimization-under-unbounded-smoothness","path":"meta_learning/bo_rep.py","file_url":"https://github.com/mingruiliu-ml-lab/bilevel-optimization-under-unbounded-smoothness/blob/HEAD/meta_learning/bo_rep.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9e863452892d1052","mcp_get_code":{"code_sha256":"9e863452892d1052"}},{"arxiv_id":"2209.08285","paper":"/paper/fr-folded-rationalization-with-a-unified","title":"FR: Folded Rationalization with a Unified Encoder","date":"2022-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kochsnow/distribution-matching-rationality","path":"core/model.py","file_url":"https://github.com/kochsnow/distribution-matching-rationality/blob/HEAD/core/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cea6a2e603a0cd85","mcp_get_code":{"code_sha256":"cea6a2e603a0cd85"}},{"arxiv_id":"2205.10664","paper":"/paper/temporal-domain-generalization-with-drift","title":"Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks","date":"2022-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baithebest/drain","path":"classification/model_elec.py","file_url":"https://github.com/baithebest/drain/blob/HEAD/classification/model_elec.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e4e2c2ef139704e7","mcp_get_code":{"code_sha256":"e4e2c2ef139704e7"}},{"arxiv_id":"2203.16084","paper":"/paper/strpm-a-spatiotemporal-residual-predictive","title":"STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction","date":"2022-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengchang467/stiphr","path":"core/models/STIP.py","file_url":"https://github.com/zhengchang467/stiphr/blob/HEAD/core/models/STIP.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eee5b7538dbe87cc","mcp_get_code":{"code_sha256":"eee5b7538dbe87cc"}},{"arxiv_id":"2111.09189","paper":"/paper/tom2c-target-oriented-multi-agent-1","title":"ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind","date":"2021-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"unrealtracking/tom2c","path":"model.py","file_url":"https://github.com/unrealtracking/tom2c/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"219b0a6e447da592","mcp_get_code":{"code_sha256":"219b0a6e447da592"}},{"arxiv_id":"2107.12262","paper":"/paper/meta-learning-adversarial-domain-adaptation","title":"Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification","date":"2021-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hccngu/MLADA","path":"src/model/modelG.py","file_url":"https://github.com/hccngu/MLADA/blob/HEAD/src/model/modelG.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fa14930edec749ff","mcp_get_code":{"code_sha256":"fa14930edec749ff"}},{"arxiv_id":"2106.06189","paper":"/paper/order-matters-probabilistic-modeling-of-node","title":"Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation","date":"2021-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tufts-ml/graph-generation-vi","path":"models/graph_rnn/model.py","file_url":"https://github.com/tufts-ml/graph-generation-vi/blob/HEAD/models/graph_rnn/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ff8f06e0f97f23f","mcp_get_code":{"code_sha256":"1ff8f06e0f97f23f"}},{"arxiv_id":"1806.09055","paper":"/paper/darts-differentiable-architecture-search","title":"DARTS: Differentiable Architecture Search","date":"2018-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anonNo2/MulTeacher-KD","path":"models/search_cnn.py","file_url":"https://github.com/anonNo2/MulTeacher-KD/blob/HEAD/models/search_cnn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7b38fef01289ef2e","mcp_get_code":{"code_sha256":"7b38fef01289ef2e"}},{"arxiv_id":"1803.11485","paper":"/paper/qmix-monotonic-value-function-factorisation","title":"QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning","date":"2018-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"starry-sky6688/marl-algorithms","path":"policy/qmix.py","file_url":"https://github.com/starry-sky6688/marl-algorithms/blob/HEAD/policy/qmix.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e0f6a55ec27b590","mcp_get_code":{"code_sha256":"9e0f6a55ec27b590"}},{"arxiv_id":"1406.1078","paper":"/paper/learning-phrase-representations-using-rnn","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","date":"2014-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kmzzhang/nbi","path":"src/nbi/nn/rnn.py","file_url":"https://github.com/kmzzhang/nbi/blob/HEAD/src/nbi/nn/rnn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"3608961b6a79dbfb","mcp_get_code":{"code_sha256":"3608961b6a79dbfb"}},{"arxiv_id":"1406.1078","paper":"/paper/learning-phrase-representations-using-rnn","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","date":"2014-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trevor-richardson/rnn_zoo","path":"models/rnn.py","file_url":"https://github.com/trevor-richardson/rnn_zoo/blob/HEAD/models/rnn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bbc681afcb7c583e","mcp_get_code":{"code_sha256":"bbc681afcb7c583e"}},{"arxiv_id":"1406.1078","paper":"/paper/learning-phrase-representations-using-rnn","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","date":"2014-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"roomylee/rnn-text-classification-tf","path":"rnn.py","file_url":"https://github.com/roomylee/rnn-text-classification-tf/blob/HEAD/rnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4dc486e7ada770a","mcp_get_code":{"code_sha256":"a4dc486e7ada770a"}}]}