{"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/get-stats","entry":"get_stats","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":37,"n_papers_ran":15,"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":34,"n_samples_ran":12,"n_samples_fingerprinted":4,"n_places":38,"n_places_pointer_only":8,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":4,"ran_fixture":1,"ran":5,"unverified":22},"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":"2604.08474","paper":"/paper/arxiv-2604-08474","title":"Quantization Impact on the Accuracy and Communication Efficiency Trade-off in Federated Learning for Aerospace Predictive Maintenance","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"therealdeadbeef/aerospace-fl-quantization","path":"plot_results.py","file_url":"https://github.com/therealdeadbeef/aerospace-fl-quantization/blob/HEAD/plot_results.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a128348e9c7a7c2b","mcp_get_code":{"code_sha256":"a128348e9c7a7c2b"}},{"arxiv_id":"2504.02922","paper":"/paper/robustly-identifying-concepts-introduced","title":"Robustly identifying concepts introduced during chat fine-tuning using crosscoders","date":"2025-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jkminder/dictionary_learning","path":"dictionary_learning/training.py","file_url":"https://github.com/jkminder/dictionary_learning/blob/HEAD/dictionary_learning/training.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"87467b7150f616a8","mcp_get_code":{"code_sha256":"87467b7150f616a8"}},{"arxiv_id":"2411.07336","paper":"/paper/setlexsem-challenge-using-set-operations-to","title":"SetLexSem Challenge: Using Set Operations to Evaluate the Lexical and Semantic Robustness of Language Models","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-science/setlexsem-challenge","path":"setlexsem/analyze/hypothesis_testing_utils.py","file_url":"https://github.com/amazon-science/setlexsem-challenge/blob/HEAD/setlexsem/analyze/hypothesis_testing_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":"5afc0bf83778d553","mcp_get_code":{"code_sha256":"5afc0bf83778d553"}},{"arxiv_id":"2411.06646","paper":"/paper/understanding-scaling-laws-with-statistical","title":"Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dahoas/transformer_manifolds_learning","path":"embeddings.py","file_url":"https://github.com/dahoas/transformer_manifolds_learning/blob/HEAD/embeddings.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"333d00475ff6db73","mcp_get_code":{"code_sha256":"333d00475ff6db73"}},{"arxiv_id":"2407.03540","paper":"/paper/comics-datasets-framework-mix-of-comics","title":"Comics Datasets Framework: Mix of Comics datasets for detection benchmarking","date":"2024-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"emanuelevivoli/CoMix","path":"comix/evaluators/detection.py","file_url":"https://github.com/emanuelevivoli/CoMix/blob/HEAD/comix/evaluators/detection.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7f4d711939fa2c85","mcp_get_code":{"code_sha256":"7f4d711939fa2c85"}},{"arxiv_id":"2406.19276","paper":"/paper/veriscore-evaluating-the-factuality-of","title":"VERISCORE: Evaluating the factuality of verifiable claims in long-form text generation","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Yixiao-Song/VeriScore","path":"veriscore/utils.py","file_url":"https://github.com/Yixiao-Song/VeriScore/blob/HEAD/veriscore/utils.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":"6b763c4109f59409","mcp_get_code":{"code_sha256":"6b763c4109f59409"}},{"arxiv_id":"2406.18521","paper":"/paper/charxiv-charting-gaps-in-realistic-chart","title":"CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-nlp/CharXiv","path":"src/get_stats.py","file_url":"https://github.com/princeton-nlp/CharXiv/blob/HEAD/src/get_stats.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"07302ba71d05d930","mcp_get_code":{"code_sha256":"07302ba71d05d930"}},{"arxiv_id":"2406.02178","paper":"/paper/audio-mamba-selective-state-spaces-for-self","title":"Audio Mamba: Selective State Spaces for Self-Supervised Audio Representations","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SarthakYadav/audio-mamba-official","path":"stats_aggregation.py","file_url":"https://github.com/SarthakYadav/audio-mamba-official/blob/HEAD/stats_aggregation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"baa71eb9105f2237","mcp_get_code":{"code_sha256":"baa71eb9105f2237"}},{"arxiv_id":"2405.16584","paper":"/paper/mentalmanip-a-dataset-for-fine-grained","title":"MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations","date":"2024-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"audreycs/MentalManip","path":"statistic_analysis/mentalManip_stats.py","file_url":"https://github.com/audreycs/MentalManip/blob/HEAD/statistic_analysis/mentalManip_stats.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"2fa574d009bd82d7","mcp_get_code":{"code_sha256":"2fa574d009bd82d7"}},{"arxiv_id":"2403.01304","paper":"/paper/improving-the-validity-of-automatically","title":"Improving the Validity of Automatically Generated Feedback via Reinforcement Learning","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"umass-ml4ed/feedback-gen-dpo","path":"analyze_reward_dataset.py","file_url":"https://github.com/umass-ml4ed/feedback-gen-dpo/blob/HEAD/analyze_reward_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1c2a77c38afe9780","mcp_get_code":{"code_sha256":"1c2a77c38afe9780"}},{"arxiv_id":"2401.09243","paper":"/paper/diffclone-enhanced-behaviour-cloning-in","title":"DiffClone: Enhanced Behaviour Cloning in Robotics with Diffusion-Driven Policy Learning","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sirabas369/DiffClone","path":"toto_benchmark/agents/Diffusion_Agent_Unet.py","file_url":"https://github.com/sirabas369/DiffClone/blob/HEAD/toto_benchmark/agents/Diffusion_Agent_Unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"62608541281b5cad","mcp_get_code":{"code_sha256":"62608541281b5cad"}},{"arxiv_id":"2401.00096","paper":"/paper/a-foundation-model-for-atomistic-materials","title":"A foundation model for atomistic materials chemistry","date":null,"month_inferred_from_arxiv_id":"2024-01","title_source":"archive","repo":"arosen93/qmof","path":"machine_learning/he_stoichiometric_45/stoich45_feature_generator.py","file_url":"https://github.com/arosen93/qmof/blob/HEAD/machine_learning/he_stoichiometric_45/stoich45_feature_generator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a96e7da4783a986","mcp_get_code":{"code_sha256":"3a96e7da4783a986"}},{"arxiv_id":"2308.03723","paper":"/paper/dimensionality-reduction-for-improving-out-of","title":"Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation","date":"2023-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mckellwoodland/dimen_reduce_mahal","path":"OOD/calc_md.py","file_url":"https://github.com/mckellwoodland/dimen_reduce_mahal/blob/HEAD/OOD/calc_md.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4d59a27c6a9afcfc","mcp_get_code":{"code_sha256":"4d59a27c6a9afcfc"}},{"arxiv_id":"2306.00942","paper":"/paper/train-offline-test-online-a-real-robot","title":"Train Offline, Test Online: A Real Robot Learning Benchmark","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AGI-Labs/toto_benchmark","path":"toto_benchmark/agents/BCAgent.py","file_url":"https://github.com/AGI-Labs/toto_benchmark/blob/HEAD/toto_benchmark/agents/BCAgent.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"62608541281b5cad","mcp_get_code":{"code_sha256":"62608541281b5cad"}},{"arxiv_id":"2305.01520","paper":"/paper/conditional-graph-information-bottleneck-for","title":"Conditional Graph Information Bottleneck for Molecular Relational Learning","date":"2023-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Namkyeong/CGIB","path":"DrugDrugInteraction/utils.py","file_url":"https://github.com/Namkyeong/CGIB/blob/HEAD/DrugDrugInteraction/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f73d7c994f64cb06","mcp_get_code":{"code_sha256":"f73d7c994f64cb06"}},{"arxiv_id":"2211.00241","paper":"/paper/adversarial-policies-beat-professional-level","title":"Adversarial Policies Beat Superhuman Go AIs","date":"2022-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alignmentresearch/go_attack","path":"plot/plot.py","file_url":"https://github.com/alignmentresearch/go_attack/blob/HEAD/plot/plot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"66383c5953d77474","mcp_get_code":{"code_sha256":"66383c5953d77474"}},{"arxiv_id":"2205.13490","paper":"/paper/semaffinet-semantic-affine-transformation-for","title":"SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation","date":"2022-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangzy22/SemAffiNet","path":"dataset/pregroup_2d_scannet.py","file_url":"https://github.com/wangzy22/SemAffiNet/blob/HEAD/dataset/pregroup_2d_scannet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b309b945f3526f50","mcp_get_code":{"code_sha256":"b309b945f3526f50"}},{"arxiv_id":"2110.02283","paper":"/paper/co-training-an-unsupervised-constituency","title":"Co-training an Unsupervised Constituency Parser with Weak Supervision","date":"2021-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"d4b3f50524ef7245","mcp_get_code":{"code_sha256":"d4b3f50524ef7245"}},{"arxiv_id":"2109.05489","paper":"/paper/illuminating-diverse-neural-cellular-automata","title":"Illuminating Diverse Neural Cellular Automata for Level Generation","date":"2021-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smearle/control-pcgrl","path":"control_pcgrl/evo/evolve.py","file_url":"https://github.com/smearle/control-pcgrl/blob/HEAD/control_pcgrl/evo/evolve.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"405a9f1e9dd6a316","mcp_get_code":{"code_sha256":"405a9f1e9dd6a316"}},{"arxiv_id":"2010.15920","paper":"/paper/recovery-rl-safe-reinforcement-learning-with","title":"Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones","date":"2020-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abalakrishna123/recovery-rl","path":"plotting/plot_runs.py","file_url":"https://github.com/abalakrishna123/recovery-rl/blob/HEAD/plotting/plot_runs.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0559500297229526","mcp_get_code":{"code_sha256":"0559500297229526"}},{"arxiv_id":"2008.06081","paper":"/paper/adversarial-training-and-provable-robustness","title":"Adversarial Training and Provable Robustness: A Tale of Two Objectives","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JmfanBU/AdvIBP","path":"IBP_Adv_Training/utils/datasets.py","file_url":"https://github.com/JmfanBU/AdvIBP/blob/HEAD/IBP_Adv_Training/utils/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1692f72216960bf3","mcp_get_code":{"code_sha256":"1692f72216960bf3"}},{"arxiv_id":"2007.15646","paper":"/paper/rewriting-a-deep-generative-model","title":"Rewriting a Deep Generative Model","date":"2020-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PeterWang512/GANSketching","path":"run_metrics.py","file_url":"https://github.com/PeterWang512/GANSketching/blob/HEAD/run_metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c04f59973771055","mcp_get_code":{"code_sha256":"0c04f59973771055"}},{"arxiv_id":"2007.15135","paper":"/paper/the-return-of-lexical-dependencies-neural","title":"The Return of Lexical Dependencies: Neural Lexicalized PCFGs","date":"2020-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhaoyanpeng/xcfg","path":"xcfg/data/baseline.py","file_url":"https://github.com/zhaoyanpeng/xcfg/blob/HEAD/xcfg/data/baseline.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d4b3f50524ef7245","mcp_get_code":{"code_sha256":"d4b3f50524ef7245"}},{"arxiv_id":"2005.08104","paper":"/paper/single-stage-semantic-segmentation-from-image","title":"Single-Stage Semantic Segmentation from Image Labels","date":"2020-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visinf/1-stage-wseg","path":"eval_seg.py","file_url":"https://github.com/visinf/1-stage-wseg/blob/HEAD/eval_seg.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":"22701581e8753c53","mcp_get_code":{"code_sha256":"22701581e8753c53"}},{"arxiv_id":"2004.06660","paper":"/paper/weight-poisoning-attacks-on-pre-trained","title":"Weight Poisoning Attacks on Pre-trained Models","date":"2020-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neulab/RIPPLe","path":"get_stats.py","file_url":"https://github.com/neulab/RIPPLe/blob/HEAD/get_stats.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"6bf281562340ed0f","mcp_get_code":{"code_sha256":"6bf281562340ed0f"}},{"arxiv_id":"2002.12920","paper":"/paper/automatic-perturbation-analysis-on-general","title":"Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond","date":"2020-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huanzhang12/CROWN-IBP","path":"datasets.py","file_url":"https://github.com/huanzhang12/CROWN-IBP/blob/HEAD/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"1692f72216960bf3","mcp_get_code":{"code_sha256":"1692f72216960bf3"}},{"arxiv_id":"2001.09212","paper":"/paper/pcgrl-procedural-content-generation-via","title":"PCGRL: Procedural Content Generation via Reinforcement Learning","date":"2020-01-24","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":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"405a9f1e9dd6a316","mcp_get_code":{"code_sha256":"405a9f1e9dd6a316"}},{"arxiv_id":"1910.13267","paper":"/paper/191013267","title":"BPE-Dropout: Simple and Effective Subword Regularization","date":"2019-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PatxiofromAlphensign/subword-nmt-mods","path":"subword_nmt/bpe_toy.py","file_url":"https://github.com/PatxiofromAlphensign/subword-nmt-mods/blob/HEAD/subword_nmt/bpe_toy.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7b94766e4ee985e","mcp_get_code":{"code_sha256":"e7b94766e4ee985e"}},{"arxiv_id":"1910.09658","paper":"/paper/optimal-power-flow-using-graph-neural","title":"Optimal Power Flow Using Graph Neural Networks","date":"2019-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tomyvazquez/doraa-uy","path":"no-supervisado/IEEE/entrenamiento/src/metric.py","file_url":"https://github.com/tomyvazquez/doraa-uy/blob/HEAD/no-supervisado/IEEE/entrenamiento/src/metric.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"77dc409702e10352","mcp_get_code":{"code_sha256":"77dc409702e10352"}},{"arxiv_id":"1910.09658","paper":"/paper/optimal-power-flow-using-graph-neural","title":"Optimal Power Flow Using Graph Neural Networks","date":"2019-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tomyvazquez/doraa-uy","path":"no-supervisado/URU/entrenamiento/src/metric.py","file_url":"https://github.com/tomyvazquez/doraa-uy/blob/HEAD/no-supervisado/URU/entrenamiento/src/metric.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"19a1e8e969cd43fe","mcp_get_code":{"code_sha256":"19a1e8e969cd43fe"}},{"arxiv_id":"1905.10615","paper":"/paper/adversarial-policies-attacking-deep","title":"Adversarial Policies: Attacking Deep Reinforcement Learning","date":"2019-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HumanCompatibleAI/adversarial-policies","path":"experiments/modelfree/highest_win_rate.py","file_url":"https://github.com/HumanCompatibleAI/adversarial-policies/blob/HEAD/experiments/modelfree/highest_win_rate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aca06d84ed47ac80","mcp_get_code":{"code_sha256":"aca06d84ed47ac80"}},{"arxiv_id":"1901.08746","paper":"/paper/biobert-a-pre-trained-biomedical-language","title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","date":"2019-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dmis-lab/bern","path":"service_checker.py","file_url":"https://github.com/dmis-lab/bern/blob/HEAD/service_checker.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"549c4d310160c2df","mcp_get_code":{"code_sha256":"549c4d310160c2df"}},{"arxiv_id":"1809.03672","paper":"/paper/deep-interest-evolution-network-for-click","title":"Deep Interest Evolution Network for Click-Through Rate Prediction","date":"2018-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kupuSs/DIEN-pipline","path":"data_process/utils.py","file_url":"https://github.com/kupuSs/DIEN-pipline/blob/HEAD/data_process/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":"ccf9fd2e5c97ebea","mcp_get_code":{"code_sha256":"ccf9fd2e5c97ebea"}},{"arxiv_id":"1703.03864","paper":"/paper/evolution-strategies-as-a-scalable","title":"Evolution Strategies as a Scalable Alternative to Reinforcement Learning","date":"2017-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"czen88/qtrader","path":"backtest.py","file_url":"https://github.com/czen88/qtrader/blob/HEAD/backtest.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":"9140472b1b7f5e55","mcp_get_code":{"code_sha256":"9140472b1b7f5e55"}},{"arxiv_id":"1611.09268","paper":"/paper/ms-marco-a-human-generated-machine-reading","title":"MS MARCO: A Human Generated MAchine Reading COmprehension Dataset","date":"2016-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/MSMARCO-Question-Answering","path":"Utils/get_stats_about_length.py","file_url":"https://github.com/microsoft/MSMARCO-Question-Answering/blob/HEAD/Utils/get_stats_about_length.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9346371b965cc92e","mcp_get_code":{"code_sha256":"9346371b965cc92e"}},{"arxiv_id":"1602.02867","paper":"/paper/value-iteration-networks","title":"Value Iteration Networks","date":"2016-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kentsommer/pytorch-value-iteration-networks","path":"utility/utils.py","file_url":"https://github.com/kentsommer/pytorch-value-iteration-networks/blob/HEAD/utility/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"1cfb1314c17b4deb","mcp_get_code":{"code_sha256":"1cfb1314c17b4deb"}},{"arxiv_id":"1508.07909","paper":"/paper/neural-machine-translation-of-rare-words-with","title":"Neural Machine Translation of Rare Words with Subword Units","date":"2015-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SeonbeomKim/Python-Byte_Pair_Encoding","path":"bpe_module/learn_BPE.py","file_url":"https://github.com/SeonbeomKim/Python-Byte_Pair_Encoding/blob/HEAD/bpe_module/learn_BPE.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"afdbbe2995c004e2","mcp_get_code":{"code_sha256":"afdbbe2995c004e2"}},{"arxiv_id":"ijcai2022_0301","paper":null,"title":"arXiv:ijcai2022_0301","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ljaiverson/pFL-APPLE","path":"utils/data_loader.py","file_url":"https://github.com/ljaiverson/pFL-APPLE/blob/HEAD/utils/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e96b25c5b0d2fa8","mcp_get_code":{"code_sha256":"0e96b25c5b0d2fa8"}}]}