{"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/identity-2","entry":"Identity","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":49,"n_papers_ran":46,"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":44,"n_samples_ran":41,"n_samples_fingerprinted":37,"n_places":50,"n_places_pointer_only":24,"by_status":{"ran_honours":1,"ran_violates":3,"ran_draft_wrong":0,"ran_fixture":0,"ran":37,"unverified":3},"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":"2505.05522","paper":"/paper/continuous-thought-machines","title":"Continuous Thought Machines","date":"2025-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SakanaAI/continuous-thought-machines","path":"models/ctm.py","file_url":"https://github.com/SakanaAI/continuous-thought-machines/blob/HEAD/models/ctm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e206223b33cedd14","mcp_get_code":{"code_sha256":"e206223b33cedd14"}},{"arxiv_id":"2504.18397","paper":"/paper/unsupervised-visual-chain-of-thought","title":"Unsupervised Visual Chain-of-Thought Reasoning via Preference Optimization","date":"2025-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kesenzhao/uv-cot","path":"omnilmm/model/omnilmm.py","file_url":"https://github.com/kesenzhao/uv-cot/blob/HEAD/omnilmm/model/omnilmm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5be6156a5bbf6aa8","mcp_get_code":{"code_sha256":"5be6156a5bbf6aa8"}},{"arxiv_id":"2504.15371","paper":"/paper/event2vec-processing-neuromorphic-events","title":"Event2Vec: Processing neuromorphic events directly by representations in vector space","date":"2025-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fangwei123456/event2vec","path":"models.py","file_url":"https://github.com/fangwei123456/event2vec/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"de6027fffe767191","mcp_get_code":{"code_sha256":"de6027fffe767191"}},{"arxiv_id":"2412.00744","paper":"/paper/a-cross-scene-benchmark-for-open-world-drone","title":"A Cross-Scene Benchmark for Open-World Drone Active Tracking","date":"2024-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SHWplus/DAT_Benchmark","path":"Alg_Base/DAT_Benchmark/envs/environment.py","file_url":"https://github.com/SHWplus/DAT_Benchmark/blob/HEAD/Alg_Base/DAT_Benchmark/envs/environment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d5f78640d43410f5","mcp_get_code":{"code_sha256":"d5f78640d43410f5"}},{"arxiv_id":"2411.16375","paper":"/paper/ca2-vdm-efficient-autoregressive-video","title":"Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing","date":"2024-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songweige/TATS","path":"tats/tats_transformer.py","file_url":"https://github.com/songweige/TATS/blob/HEAD/tats/tats_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f020bb1b02990123","mcp_get_code":{"code_sha256":"f020bb1b02990123"}},{"arxiv_id":"2411.07231","paper":"/paper/watermark-anything-with-localized-messages","title":"Watermark Anything with Localized Messages","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/watermark-anything","path":"watermark_anything/models/wam.py","file_url":"https://github.com/facebookresearch/watermark-anything/blob/HEAD/watermark_anything/models/wam.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bcb4b3ed6a29fdd0","mcp_get_code":{"code_sha256":"bcb4b3ed6a29fdd0"}},{"arxiv_id":"2410.02543","paper":"/paper/diffusion-models-are-evolutionary-algorithms","title":"Diffusion Models are Evolutionary Algorithms","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Zhangyanbo/diffusion-evolution","path":"diffevo/optimizer.py","file_url":"https://github.com/Zhangyanbo/diffusion-evolution/blob/HEAD/diffevo/optimizer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5e3a40fa59c113e0","mcp_get_code":{"code_sha256":"5e3a40fa59c113e0"}},{"arxiv_id":"2406.09329","paper":"/paper/is-value-learning-really-the-main-bottleneck","title":"Is Value Learning Really the Main Bottleneck in Offline RL?","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seohongpark/ogbench","path":"impls/agents/hiql.py","file_url":"https://github.com/seohongpark/ogbench/blob/HEAD/impls/agents/hiql.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"55eac5ab51a6832f","mcp_get_code":{"code_sha256":"55eac5ab51a6832f"}},{"arxiv_id":"2404.01524","paper":"/paper/on-train-test-class-overlap-and-detection-for","title":"On Train-Test Class Overlap and Detection for Image Retrieval","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MCC-WH/Token","path":"networks/RetrievalNet.py","file_url":"https://github.com/MCC-WH/Token/blob/HEAD/networks/RetrievalNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"acdcf1b7fddda414","mcp_get_code":{"code_sha256":"acdcf1b7fddda414"}},{"arxiv_id":"2403.12553","paper":"/paper/pretraining-codomain-attention-neural","title":"Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ashiq24/coda-no","path":"layers/codano_block_nd.py","file_url":"https://github.com/ashiq24/coda-no/blob/HEAD/layers/codano_block_nd.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8c3830c9f8a37f06","mcp_get_code":{"code_sha256":"8c3830c9f8a37f06"}},{"arxiv_id":"2403.08161","paper":"/paper/lafs-landmark-based-facial-self-supervised","title":"LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition","date":"2024-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"szlbiubiubiu/lafs_cvpr2024","path":"face_pre_pro/ViT_face.py","file_url":"https://github.com/szlbiubiubiu/lafs_cvpr2024/blob/HEAD/face_pre_pro/ViT_face.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"897a6cef897cd588","mcp_get_code":{"code_sha256":"897a6cef897cd588"}},{"arxiv_id":"2309.04747","paper":"/paper/when-to-learn-what-model-adaptive-data","title":"When to Learn What: Model-Adaptive Data Augmentation Curriculum","date":"2023-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JackHck/MADAug","path":"adaptive_augmentor.py","file_url":"https://github.com/JackHck/MADAug/blob/HEAD/adaptive_augmentor.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5adb748bc6564a2e","mcp_get_code":{"code_sha256":"5adb748bc6564a2e"}},{"arxiv_id":"2306.16058","paper":"/paper/duet-2d-structured-and-approximately","title":"DUET: 2D Structured and Approximately Equivariant Representations","date":"2023-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apple/ml-duet","path":"duet/models/duet.py","file_url":"https://github.com/apple/ml-duet/blob/HEAD/duet/models/duet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4f7e1c08346a6301","mcp_get_code":{"code_sha256":"4f7e1c08346a6301"}},{"arxiv_id":"2306.05031","paper":"/paper/generalizable-lightweight-proxy-for-robust-1","title":"Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations","date":"2023-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hyeonjeongha/croze","path":"zero_cost_methods/pruners/measures/croze.py","file_url":"https://github.com/hyeonjeongha/croze/blob/HEAD/zero_cost_methods/pruners/measures/croze.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4a47afcc6d8693f8","mcp_get_code":{"code_sha256":"4a47afcc6d8693f8"}},{"arxiv_id":"2303.10353","paper":"/paper/sharpness-aware-gradient-matching-for-domain","title":"Sharpness-Aware Gradient Matching for Domain Generalization","date":"2023-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wang-pengfei/sagm","path":"domainbed/algorithms/algorithms.py","file_url":"https://github.com/wang-pengfei/sagm/blob/HEAD/domainbed/algorithms/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c090fe2ca8acedb3","mcp_get_code":{"code_sha256":"c090fe2ca8acedb3"}},{"arxiv_id":"2209.14464","paper":"/paper/neural-methods-for-logical-reasoning-over-1","title":"Neural Methods for Logical Reasoning Over Knowledge Graphs","date":"2022-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amayuelas/NNKGReasoning","path":"models/baselines.py","file_url":"https://github.com/amayuelas/NNKGReasoning/blob/HEAD/models/baselines.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f44985272d18127a","mcp_get_code":{"code_sha256":"f44985272d18127a"}},{"arxiv_id":"2208.00850","paper":"/paper/subgraph-neighboring-relations-infomax-for","title":"Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs","date":"2022-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tebmer/SNRI","path":"model/dgl/graph_classifier.py","file_url":"https://github.com/Tebmer/SNRI/blob/HEAD/model/dgl/graph_classifier.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"df20ae228378ef18","mcp_get_code":{"code_sha256":"df20ae228378ef18"}},{"arxiv_id":"2206.09959","paper":"/paper/global-context-vision-transformers","title":"Global Context Vision Transformers","date":"2022-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awsaf49/gcvit-tf","path":"gcvit/models/gcvit.py","file_url":"https://github.com/awsaf49/gcvit-tf/blob/HEAD/gcvit/models/gcvit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc4f231a89851495","mcp_get_code":{"code_sha256":"fc4f231a89851495"}},{"arxiv_id":"2206.00529","paper":"/paper/variance-reduction-is-an-antidote-to","title":"Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top","date":"2022-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samuelhorvath/vr_byzantine","path":"code/worker.py","file_url":"https://github.com/samuelhorvath/vr_byzantine/blob/HEAD/code/worker.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1b9d3a564242e743","mcp_get_code":{"code_sha256":"1b9d3a564242e743"}},{"arxiv_id":"2205.14546","paper":"/paper/the-missing-invariance-principle-found-the","title":"The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization","date":"2022-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm/mri","path":"custom_algorithms.py","file_url":"https://github.com/ibm/mri/blob/HEAD/custom_algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f0e9d2dc26748363","mcp_get_code":{"code_sha256":"f0e9d2dc26748363"}},{"arxiv_id":"2110.13522","paper":"/paper/probabilistic-entity-representation-model-for","title":"Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akirato/perm-gaussiankg","path":"models_gaussian.py","file_url":"https://github.com/akirato/perm-gaussiankg/blob/HEAD/models_gaussian.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"f44985272d18127a","mcp_get_code":{"code_sha256":"f44985272d18127a"}},{"arxiv_id":"2110.13430","paper":"/paper/contextual-similarity-aggregation-with-self","title":"Contextual Similarity Aggregation with Self-attention for Visual Re-ranking","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mcc-wh/csa","path":"network/RerankTransformer.py","file_url":"https://github.com/mcc-wh/csa/blob/HEAD/network/RerankTransformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f343584e73054bcf","mcp_get_code":{"code_sha256":"f343584e73054bcf"}},{"arxiv_id":"2105.12151","paper":"/paper/autorecon-neural-architecture-search-based","title":"AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression","date":"2021-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iamkanghyunchoi/ait","path":"AutoReCon_AIT/model_generator.py","file_url":"https://github.com/iamkanghyunchoi/ait/blob/HEAD/AutoReCon_AIT/model_generator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"5094999a837b9555","mcp_get_code":{"code_sha256":"5094999a837b9555"}},{"arxiv_id":"2104.06650","paper":"/paper/learning-semantic-person-image-generation-by","title":"Learning Semantic Person Image Generation by Region-Adaptive Normalization","date":"2021-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cszy98/SPGNet","path":"models/SPG_net_deepfashion.py","file_url":"https://github.com/cszy98/SPGNet/blob/HEAD/models/SPG_net_deepfashion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3781852db9479c80","mcp_get_code":{"code_sha256":"3781852db9479c80"}},{"arxiv_id":"2103.17022","paper":"/paper/layout-guided-novel-view-synthesis-from-a","title":"Layout-Guided Novel View Synthesis from a Single Indoor Panorama","date":"2021-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/synsin","path":"models/z_buffermodel.py","file_url":"https://github.com/facebookresearch/synsin/blob/HEAD/models/z_buffermodel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"3db9a75f55277141","mcp_get_code":{"code_sha256":"3db9a75f55277141"}},{"arxiv_id":"2103.14030","paper":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nathanlem1/igae-net","path":"model/models.py","file_url":"https://github.com/nathanlem1/igae-net/blob/HEAD/model/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"26120e2844a90ffb","mcp_get_code":{"code_sha256":"26120e2844a90ffb"}},{"arxiv_id":"2103.13253","paper":"/paper/learning-versatile-neural-architectures-by","title":"Learning Versatile Neural Architectures by Propagating Network Codes","date":"2021-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingmyu/NCP","path":"models/supernet.py","file_url":"https://github.com/dingmyu/NCP/blob/HEAD/models/supernet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"eb4511eaa68b7bd7","mcp_get_code":{"code_sha256":"eb4511eaa68b7bd7"}},{"arxiv_id":"2103.02406","paper":"/paper/multi-attentional-deepfake-detection","title":"Multi-attentional Deepfake Detection","date":"2021-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yoctta/multiple-attention","path":"models/MAT.py","file_url":"https://github.com/yoctta/multiple-attention/blob/HEAD/models/MAT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"552d3aca78645318","mcp_get_code":{"code_sha256":"552d3aca78645318"}},{"arxiv_id":"2103.02376","paper":"/paper/event-based-synthetic-aperture-imaging","title":"Event-based Synthetic Aperture Imaging with a Hybrid Network","date":"2021-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvs-whu/E-SAI","path":"codes/Networks/Hybrid.py","file_url":"https://github.com/dvs-whu/E-SAI/blob/HEAD/codes/Networks/Hybrid.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"695cdd940a3e5a2e","mcp_get_code":{"code_sha256":"695cdd940a3e5a2e"}},{"arxiv_id":"2103.00418","paper":"/paper/logic-embeddings-for-complex-query-answering","title":"Logic Embeddings for Complex Query Answering","date":"2021-02-28","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":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"f44985272d18127a","mcp_get_code":{"code_sha256":"f44985272d18127a"}},{"arxiv_id":"2010.11465","paper":"/paper/beta-embeddings-for-multi-hop-logical","title":"Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snap-stanford/KGReasoning","path":"models.py","file_url":"https://github.com/snap-stanford/KGReasoning/blob/HEAD/models.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f44985272d18127a","mcp_get_code":{"code_sha256":"f44985272d18127a"}},{"arxiv_id":"2010.04505","paper":"/paper/self-paced-learning-for-neural-machine","title":"Self-Paced Learning for Neural Machine Translation","date":"2020-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wanyu2018umac/Self-Paced-Learning-for-Neural-Machine-Translation","path":"train/train_single.py","file_url":"https://github.com/wanyu2018umac/Self-Paced-Learning-for-Neural-Machine-Translation/blob/HEAD/train/train_single.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4831d28d3db6715d","mcp_get_code":{"code_sha256":"4831d28d3db6715d"}},{"arxiv_id":"2006.15646","paper":"/paper/characterizing-the-expressive-power-of","title":"Expressive Power of Invariant and Equivariant Graph Neural Networks","date":"2020-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlelarge/graph_neural_net","path":"models/blocks_emb.py","file_url":"https://github.com/mlelarge/graph_neural_net/blob/HEAD/models/blocks_emb.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9375f2695dc66fe0","mcp_get_code":{"code_sha256":"9375f2695dc66fe0"}},{"arxiv_id":"2006.14154","paper":"/paper/strictly-batch-imitation-learning-by-energy","title":"Strictly Batch Imitation Learning by Energy-based Distribution Matching","date":"2020-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wgrathwohl/JEM","path":"train_wrn_ebm.py","file_url":"https://github.com/wgrathwohl/JEM/blob/HEAD/train_wrn_ebm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fdbb7a963e661fb1","mcp_get_code":{"code_sha256":"fdbb7a963e661fb1"}},{"arxiv_id":"2006.13662","paper":"/paper/labelling-unlabelled-videos-from-scratch-with","title":"Labelling unlabelled videos from scratch with multi-modal self-supervision","date":"2020-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/selavi","path":"model.py","file_url":"https://github.com/facebookresearch/selavi/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"cd43c253a0e41d2f","mcp_get_code":{"code_sha256":"cd43c253a0e41d2f"}},{"arxiv_id":"2006.12862","paper":"/paper/automatic-data-augmentation-for","title":"Automatic Data Augmentation for Generalization in Deep Reinforcement Learning","date":"2020-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rraileanu/auto-drac","path":"data_augs.py","file_url":"https://github.com/rraileanu/auto-drac/blob/HEAD/data_augs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a8627e2fd47595ae","mcp_get_code":{"code_sha256":"a8627e2fd47595ae"}},{"arxiv_id":"2004.07802","paper":"/paper/geometry-aware-gradient-algorithms-for-neural","title":"Geometry-Aware Gradient Algorithms for Neural Architecture Search","date":"2020-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NivNayman/XNAS","path":"model.py","file_url":"https://github.com/NivNayman/XNAS/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c6c6288219c9d532","mcp_get_code":{"code_sha256":"c6c6288219c9d532"}},{"arxiv_id":"2002.05969","paper":"/paper/query2box-reasoning-over-knowledge-graphs-in-1","title":"Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings","date":"2020-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kjh9503/caqr","path":"CaQR/betae/models.py","file_url":"https://github.com/kjh9503/caqr/blob/HEAD/CaQR/betae/models.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f44985272d18127a","mcp_get_code":{"code_sha256":"f44985272d18127a"}},{"arxiv_id":"2002.05709","paper":"/paper/a-simple-framework-for-contrastive-learning","title":"A Simple Framework for Contrastive Learning of Visual Representations","date":"2020-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"talipucar/PyFlow_SimCLR","path":"src/model.py","file_url":"https://github.com/talipucar/PyFlow_SimCLR/blob/HEAD/src/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b77efc36f9b77b9b","mcp_get_code":{"code_sha256":"b77efc36f9b77b9b"}},{"arxiv_id":"2001.07685","paper":"/paper/fixmatch-simplifying-semi-supervised-learning","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","date":"2020-01-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kekmodel/FixMatch-pytorch","path":"dataset/randaugment.py","file_url":"https://github.com/kekmodel/FixMatch-pytorch/blob/HEAD/dataset/randaugment.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0847c9a3c04c392e","mcp_get_code":{"code_sha256":"0847c9a3c04c392e"}},{"arxiv_id":"1909.13719","paper":"/paper/randaugment-practical-data-augmentation-with","title":"RandAugment: Practical automated data augmentation with a reduced search space","date":"2019-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"etetteh/sota-data-augmentation-and-optimizers","path":"augmentation/RandAugment.py","file_url":"https://github.com/etetteh/sota-data-augmentation-and-optimizers/blob/HEAD/augmentation/RandAugment.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08f47bae89750cac","mcp_get_code":{"code_sha256":"08f47bae89750cac"}},{"arxiv_id":"1909.05311","paper":"/paper/graph-based-reasoning-over-heterogeneous","title":"Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering","date":"2019-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DecstionBack/AAAI_2020_CommonsenseQA","path":"pytorch_transformers/modeling_utils.py","file_url":"https://github.com/DecstionBack/AAAI_2020_CommonsenseQA/blob/HEAD/pytorch_transformers/modeling_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f44985272d18127a","mcp_get_code":{"code_sha256":"f44985272d18127a"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shijianjian/efficientnet-pytorch-3d","path":"efficientnet_pytorch_3d/model.py","file_url":"https://github.com/shijianjian/efficientnet-pytorch-3d/blob/HEAD/efficientnet_pytorch_3d/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b308ef01620c79cd","mcp_get_code":{"code_sha256":"b308ef01620c79cd"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0","path":"utils/efficientnet_pytorch/model.py","file_url":"https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0/blob/HEAD/utils/efficientnet_pytorch/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"59c76d4ae50a8e09","mcp_get_code":{"code_sha256":"59c76d4ae50a8e09"}},{"arxiv_id":"1905.02244","paper":"/paper/searching-for-mobilenetv3","title":"Searching for MobileNetV3","date":"2019-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chris-boson/fashion_mnist","path":"trainer/models/mobilenetv3.py","file_url":"https://github.com/chris-boson/fashion_mnist/blob/HEAD/trainer/models/mobilenetv3.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9942e2eaf6ad275b","mcp_get_code":{"code_sha256":"9942e2eaf6ad275b"}},{"arxiv_id":"1806.01445","paper":"/paper/embedding-logical-queries-on-knowledge-graphs","title":"Embedding Logical Queries on Knowledge Graphs","date":"2018-06-05","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":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"f44985272d18127a","mcp_get_code":{"code_sha256":"f44985272d18127a"}},{"arxiv_id":"1802.03268","paper":"/paper/efficient-neural-architecture-search-via-1","title":"Efficient Neural Architecture Search via Parameter Sharing","date":"2018-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zbyte64/pytorch-dagsearch","path":"dagsearch/dag.py","file_url":"https://github.com/zbyte64/pytorch-dagsearch/blob/HEAD/dagsearch/dag.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cefafcb412701ad5","mcp_get_code":{"code_sha256":"cefafcb412701ad5"}},{"arxiv_id":"1711.00851","paper":"/paper/provable-defenses-against-adversarial","title":"Provable defenses against adversarial examples via the convex outer adversarial polytope","date":"2017-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fra31/mmr-universal","path":"kolter_wong/convex_adversarial/dual_network.py","file_url":"https://github.com/fra31/mmr-universal/blob/HEAD/kolter_wong/convex_adversarial/dual_network.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"15451c00600189d1","mcp_get_code":{"code_sha256":"15451c00600189d1"}},{"arxiv_id":"1503.05671","paper":"/paper/optimizing-neural-networks-with-kronecker","title":"Optimizing Neural Networks with Kronecker-factored Approximate Curvature","date":"2015-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"n-gao/pytorch-kfac","path":"torch_kfac/kfac_optimizer.py","file_url":"https://github.com/n-gao/pytorch-kfac/blob/HEAD/torch_kfac/kfac_optimizer.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":"16eaade9680fa2ad","mcp_get_code":{"code_sha256":"16eaade9680fa2ad"}},{"arxiv_id":"2024.findings-acl.859","paper":null,"title":"arXiv:2024.findings-acl.859","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"1emonx/Amanda","path":"models/amanda_model.py","file_url":"https://github.com/1emonx/Amanda/blob/HEAD/models/amanda_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1c55c49da31bc6fa","mcp_get_code":{"code_sha256":"1c55c49da31bc6fa"}}]}