{"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-paths","entry":"get_paths","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":32,"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":35,"n_samples_ran":11,"n_samples_fingerprinted":2,"n_places":37,"n_places_pointer_only":12,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":9,"unverified":24},"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":"2604.15771","paper":"/paper/arxiv-2604-15771","title":"Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"weikai202/SkillRAG","path":"balance_train_dataset.py","file_url":"https://github.com/weikai202/SkillRAG/blob/HEAD/balance_train_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b27aad01e7b1c863","mcp_get_code":{"code_sha256":"b27aad01e7b1c863"}},{"arxiv_id":"2410.05289","paper":"/paper/mars-a-neurosymbolic-approach-for","title":"MARS: A neurosymbolic approach for interpretable drug discovery","date":"2024-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"laurendelong21/mars","path":"MARS/results/path_utils.py","file_url":"https://github.com/laurendelong21/mars/blob/HEAD/MARS/results/path_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":"79ee91714f0aff89","mcp_get_code":{"code_sha256":"79ee91714f0aff89"}},{"arxiv_id":"2406.07217","paper":"/paper/a-synthetic-dataset-for-personal-attribute","title":"A Synthetic Dataset for Personal Attribute Inference","date":"2024-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eth-sri/llm-anonymization","path":"src/anonymized/plot_anonymized.py","file_url":"https://github.com/eth-sri/llm-anonymization/blob/HEAD/src/anonymized/plot_anonymized.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a0936369faa9744b","mcp_get_code":{"code_sha256":"a0936369faa9744b"}},{"arxiv_id":"2404.10438","paper":"/paper/the-unreasonable-effectiveness-of-pre-trained","title":"The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement","date":"2024-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ga1i13o/mcloc_poseref","path":"path_configs.py","file_url":"https://github.com/ga1i13o/mcloc_poseref/blob/HEAD/path_configs.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f0385c34984c526e","mcp_get_code":{"code_sha256":"f0385c34984c526e"}},{"arxiv_id":"2404.06139","paper":"/paper/diffharmony-latent-diffusion-model-meets","title":"DiffHarmony: Latent Diffusion Model Meets Image Harmonization","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nicecv/diffharmony","path":"src/utils.py","file_url":"https://github.com/nicecv/diffharmony/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f2e5b96191ed112e","mcp_get_code":{"code_sha256":"f2e5b96191ed112e"}},{"arxiv_id":"2404.06139","paper":"/paper/diffharmony-latent-diffusion-model-meets","title":"DiffHarmony: Latent Diffusion Model Meets Image Harmonization","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nicecv/diffharmony","path":"src/dataset/ihd_dataset.py","file_url":"https://github.com/nicecv/diffharmony/blob/HEAD/src/dataset/ihd_dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e00c6f7b7efc9499","mcp_get_code":{"code_sha256":"e00c6f7b7efc9499"}},{"arxiv_id":"2402.01399","paper":"/paper/a-probabilistic-model-to-explain-self","title":"A Probabilistic Model Behind Self-Supervised Learning","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alicebizeul/simvae","path":"src/utils/utils.py","file_url":"https://github.com/alicebizeul/simvae/blob/HEAD/src/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"56ff7cdb48ee85b3","mcp_get_code":{"code_sha256":"56ff7cdb48ee85b3"}},{"arxiv_id":"2312.03533","paper":"/paper/low-shot-object-learning-with-mutual-1","title":"Low-shot Object Learning with Mutual Exclusivity Bias","date":"2023-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rehg-lab/LSME","path":"data_utils/data_utils.py","file_url":"https://github.com/rehg-lab/LSME/blob/HEAD/data_utils/data_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"32f1ebc2d1f583c0","mcp_get_code":{"code_sha256":"32f1ebc2d1f583c0"}},{"arxiv_id":"2311.09731","paper":"/paper/prudent-silence-or-foolish-babble-examining","title":"Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"genglinliu/unknownbench","path":"src/run_mistral.py","file_url":"https://github.com/genglinliu/unknownbench/blob/HEAD/src/run_mistral.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5da5cbdc6a11455a","mcp_get_code":{"code_sha256":"5da5cbdc6a11455a"}},{"arxiv_id":"2311.09731","paper":"/paper/prudent-silence-or-foolish-babble-examining","title":"Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"genglinliu/unknownbench","path":"src/unified_verbalized_confidence_regression.py","file_url":"https://github.com/genglinliu/unknownbench/blob/HEAD/src/unified_verbalized_confidence_regression.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1328167b94bcaec9","mcp_get_code":{"code_sha256":"1328167b94bcaec9"}},{"arxiv_id":"2311.09731","paper":"/paper/prudent-silence-or-foolish-babble-examining","title":"Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"genglinliu/unknownbench","path":"src/run_llama_and_vicuna.py","file_url":"https://github.com/genglinliu/unknownbench/blob/HEAD/src/run_llama_and_vicuna.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ff08724afec72f1d","mcp_get_code":{"code_sha256":"ff08724afec72f1d"}},{"arxiv_id":"2309.11325","paper":"/paper/disc-lawllm-fine-tuning-large-language-models","title":"DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services","date":"2023-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fudandisc/disc-lawllm","path":"eval/src/utils.py","file_url":"https://github.com/fudandisc/disc-lawllm/blob/HEAD/eval/src/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":"9358925191a9836e","mcp_get_code":{"code_sha256":"9358925191a9836e"}},{"arxiv_id":"2306.08789","paper":"/paper/efficient-token-guided-image-text-retrieval","title":"Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lcfractal/tgdt","path":"data.py","file_url":"https://github.com/lcfractal/tgdt/blob/HEAD/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"66f891f51e3bec44","mcp_get_code":{"code_sha256":"66f891f51e3bec44"}},{"arxiv_id":"2301.02039","paper":"/paper/randomized-message-interception-smoothing","title":"Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks","date":"2023-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yascho/interception_smoothing","path":"interception_smoothing/cert.py","file_url":"https://github.com/yascho/interception_smoothing/blob/HEAD/interception_smoothing/cert.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"644cb644cd0d6d27","mcp_get_code":{"code_sha256":"644cb644cd0d6d27"}},{"arxiv_id":"2206.00484","paper":"/paper/dep-rl-embodied-exploration-for-reinforcement","title":"DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems","date":"2022-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"martius-lab/depRL","path":"deprl/play.py","file_url":"https://github.com/martius-lab/depRL/blob/HEAD/deprl/play.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e825a9bd6f6bcd0","mcp_get_code":{"code_sha256":"6e825a9bd6f6bcd0"}},{"arxiv_id":"2202.08303","paper":"/paper/openkbp-opt-an-international-and-reproducible","title":"OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines","date":"2022-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ababier/open-kbp-opt","path":"provided_code/general_functions.py","file_url":"https://github.com/ababier/open-kbp-opt/blob/HEAD/provided_code/general_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6c4f4e699e9e2f8d","mcp_get_code":{"code_sha256":"6c4f4e699e9e2f8d"}},{"arxiv_id":"2111.09356","paper":"/paper/charting-and-navigating-the-space-of","title":"Charting and navigating the space of solutions for recurrent neural networks","date":"2021-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eliaturner/space-of-solutions-rnn","path":"analysis/dynamics_to_graph.py","file_url":"https://github.com/eliaturner/space-of-solutions-rnn/blob/HEAD/analysis/dynamics_to_graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"65f3109a43521871","mcp_get_code":{"code_sha256":"65f3109a43521871"}},{"arxiv_id":"2010.15440","paper":"/paper/flatnet-towards-photorealistic-scene","title":"FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements","date":"2020-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"siddiquesalman/flatnet","path":"dataloader.py","file_url":"https://github.com/siddiquesalman/flatnet/blob/HEAD/dataloader.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":"bbf2af5cbb114ceb","mcp_get_code":{"code_sha256":"bbf2af5cbb114ceb"}},{"arxiv_id":"2008.05231","paper":"/paper/fine-grained-visual-textual-alignment-for","title":"Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders","date":"2020-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mesnico/TERAN","path":"data.py","file_url":"https://github.com/mesnico/TERAN/blob/HEAD/data.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":"66f891f51e3bec44","mcp_get_code":{"code_sha256":"66f891f51e3bec44"}},{"arxiv_id":"2007.03669","paper":"/paper/see-hear-explore-curiosity-via-audio-visual","title":"See, Hear, Explore: Curiosity via Audio-Visual Association","date":"2020-07-07","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":"2cd075f28d3a33a3","mcp_get_code":{"code_sha256":"2cd075f28d3a33a3"}},{"arxiv_id":"2005.11401","paper":"/paper/retrieval-augmented-generation-for-knowledge","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","date":"2020-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"docarray/docarray","path":"docarray/helper.py","file_url":"https://github.com/docarray/docarray/blob/HEAD/docarray/helper.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":"f54f8f538675cd65","mcp_get_code":{"code_sha256":"f54f8f538675cd65"}},{"arxiv_id":"2004.09144","paper":"/paper/transformer-reasoning-network-for-image-text","title":"Transformer Reasoning Network for Image-Text Matching and Retrieval","date":"2020-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mesnico/TERN","path":"data.py","file_url":"https://github.com/mesnico/TERN/blob/HEAD/data.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":"ae1d8e2720a00989","mcp_get_code":{"code_sha256":"ae1d8e2720a00989"}},{"arxiv_id":"1909.05506","paper":"/paper/camp-cross-modal-adaptive-message-passing-for","title":"CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval","date":"2019-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZihaoWang-CV/CAMP_iccv19","path":"data.py","file_url":"https://github.com/ZihaoWang-CV/CAMP_iccv19/blob/HEAD/data.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":"99e6f6b6e105a9a8","mcp_get_code":{"code_sha256":"99e6f6b6e105a9a8"}},{"arxiv_id":"1906.04402","paper":"/paper/polysemous-visual-semantic-embedding-for-1","title":"Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval","date":"2019-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yalesong/pvse","path":"data.py","file_url":"https://github.com/yalesong/pvse/blob/HEAD/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9919713948f52bff","mcp_get_code":{"code_sha256":"9919713948f52bff"}},{"arxiv_id":"1905.06873","paper":"/paper/das3h-modeling-student-learning-and","title":"DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills","date":"2019-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jilljenn/ktm","path":"dataio.py","file_url":"https://github.com/jilljenn/ktm/blob/HEAD/dataio.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e1af9b2f8b525b2","mcp_get_code":{"code_sha256":"3e1af9b2f8b525b2"}},{"arxiv_id":"1905.00378","paper":"/paper/gradient-free-activation-maximization-for","title":"Gradient-free activation maximization for identifying effective stimuli","date":"2019-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"willwx/XDream","path":"xdream/net_utils/net_loader.py","file_url":"https://github.com/willwx/XDream/blob/HEAD/xdream/net_utils/net_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5f288d9caa6dfb91","mcp_get_code":{"code_sha256":"5f288d9caa6dfb91"}},{"arxiv_id":"1808.04355","paper":"/paper/large-scale-study-of-curiosity-driven","title":"Large-Scale Study of Curiosity-Driven Learning","date":"2018-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vdean/audio-curiosity","path":"make_plots.py","file_url":"https://github.com/vdean/audio-curiosity/blob/HEAD/make_plots.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2cd075f28d3a33a3","mcp_get_code":{"code_sha256":"2cd075f28d3a33a3"}},{"arxiv_id":"1807.11176","paper":"/paper/human-motion-analysis-with-deep-metric","title":"Human Motion Analysis with Deep Metric Learning","date":"2018-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dhesenkamp/attentive-lstm","path":"util.py","file_url":"https://github.com/dhesenkamp/attentive-lstm/blob/HEAD/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"224ff12b736dcb86","mcp_get_code":{"code_sha256":"224ff12b736dcb86"}},{"arxiv_id":"1806.10348","paper":"/paper/learning-visually-grounded-semantics-from","title":"Learning Visually-Grounded Semantics from Contrastive Adversarial Samples","date":"2018-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ExplorerFreda/VSE-C","path":"VSE_C/data.py","file_url":"https://github.com/ExplorerFreda/VSE-C/blob/HEAD/VSE_C/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b61084903f7e3069","mcp_get_code":{"code_sha256":"b61084903f7e3069"}},{"arxiv_id":"1803.05337","paper":"/paper/learning-to-recognize-musical-genre-from","title":"Learning to Recognize Musical Genre from Audio","date":"2018-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"raonsol/deep-pitcher","path":"utils.py","file_url":"https://github.com/raonsol/deep-pitcher/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5150c03fa3be092b","mcp_get_code":{"code_sha256":"5150c03fa3be092b"}},{"arxiv_id":"1603.08511","paper":"/paper/colorful-image-colorization","title":"Colorful Image Colorization","date":"2016-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Callifrey/Paddle-CIC","path":"utils.py","file_url":"https://github.com/Callifrey/Paddle-CIC/blob/HEAD/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":"7995e2bd8e1c55d9","mcp_get_code":{"code_sha256":"7995e2bd8e1c55d9"}},{"arxiv_id":"1510.03055","paper":"/paper/a-diversity-promoting-objective-function-for","title":"A Diversity-Promoting Objective Function for Neural Conversation Models","date":"2015-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UladzimirBaklan/qwerty","path":"chatbot.py","file_url":"https://github.com/UladzimirBaklan/qwerty/blob/HEAD/chatbot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cf672035bb7ea7a3","mcp_get_code":{"code_sha256":"cf672035bb7ea7a3"}},{"arxiv_id":"1510.03055","paper":"/paper/a-diversity-promoting-objective-function-for","title":"A Diversity-Promoting Objective Function for Neural Conversation Models","date":"2015-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manu-sk/rnn_chatbot","path":"chatbot.py","file_url":"https://github.com/manu-sk/rnn_chatbot/blob/HEAD/chatbot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d14327156e4feb9e","mcp_get_code":{"code_sha256":"d14327156e4feb9e"}},{"arxiv_id":"1503.03832","paper":"/paper/facenet-a-unified-embedding-for-face","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","date":"2015-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davidsandberg/facenet","path":"src/lfw.py","file_url":"https://github.com/davidsandberg/facenet/blob/HEAD/src/lfw.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86bfef7c19607cea","mcp_get_code":{"code_sha256":"86bfef7c19607cea"}},{"arxiv_id":"1503.03832","paper":"/paper/facenet-a-unified-embedding-for-face","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","date":"2015-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eewindfly/facenet","path":"src/lfw.py","file_url":"https://github.com/eewindfly/facenet/blob/HEAD/src/lfw.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4eb25d391115d3f","mcp_get_code":{"code_sha256":"e4eb25d391115d3f"}},{"arxiv_id":"ijcai2022_0136","paper":null,"title":"arXiv:ijcai2022_0136","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"weii41392/AQT","path":"datasets/DAOD.py","file_url":"https://github.com/weii41392/AQT/blob/HEAD/datasets/DAOD.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"df502b8889fc3ed1","mcp_get_code":{"code_sha256":"df502b8889fc3ed1"}},{"arxiv_id":"aaai_35028","paper":null,"title":"arXiv:aaai_35028","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Hutchinson-Lab/Data-Augmentation-Approaches-for-Satellite-Imagery","path":"paths.py","file_url":"https://github.com/Hutchinson-Lab/Data-Augmentation-Approaches-for-Satellite-Imagery/blob/HEAD/paths.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"670b067c3e38f54e","mcp_get_code":{"code_sha256":"670b067c3e38f54e"}}]}