{"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/encoderblock","entry":"EncoderBlock","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":23,"n_papers_ran":16,"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":33,"n_samples_ran":22,"n_samples_fingerprinted":2,"n_places":33,"n_places_pointer_only":15,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":22,"unverified":11},"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":"2607.11656","paper":"/paper/arxiv-2607-11656","title":"Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"cschneuw/nitrogen","path":"src/model/naim.py","file_url":"https://github.com/cschneuw/nitrogen/blob/HEAD/src/model/naim.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"baf678781627e640","mcp_get_code":{"code_sha256":"baf678781627e640"}},{"arxiv_id":"2603.03312","paper":"/paper/arxiv-2603-03312","title":"Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"xmed-lab/SemKey","path":"model/semkey_parallel.py","file_url":"https://github.com/xmed-lab/SemKey/blob/HEAD/model/semkey_parallel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e34125eeae114501","mcp_get_code":{"code_sha256":"e34125eeae114501"}},{"arxiv_id":"2512.20251","paper":"/paper/arxiv-2512-20251","title":"Degradation-Aware Metric Prompting for Hyperspectral Image Restoration","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"MiliLab/DAMP","path":"DAMP.py","file_url":"https://github.com/MiliLab/DAMP/blob/HEAD/DAMP.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"283eeaddfa304bba","mcp_get_code":{"code_sha256":"283eeaddfa304bba"}},{"arxiv_id":"2508.16112","paper":"/paper/arxiv-2508-16112","title":"IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"HeewoongNoh/IR-Agent","path":"models/translator.py","file_url":"https://github.com/HeewoongNoh/IR-Agent/blob/HEAD/models/translator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"23282f4dda20adf4","mcp_get_code":{"code_sha256":"23282f4dda20adf4"}},{"arxiv_id":"2507.00880","paper":null,"title":"arXiv:2507.00880","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"XuRuihan/NNFormer","path":"neuralformer/models/encoders/neuralformer.py","file_url":"https://github.com/XuRuihan/NNFormer/blob/HEAD/neuralformer/models/encoders/neuralformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4720d099da8a194","mcp_get_code":{"code_sha256":"b4720d099da8a194"}},{"arxiv_id":"2503.17716","paper":"/paper/emplace-self-supervised-urban-scene-change","title":"EMPLACE: Self-Supervised Urban Scene Change Detection","date":"2025-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Timalph/EMPLACE","path":"Rectangular_VIT.py","file_url":"https://github.com/Timalph/EMPLACE/blob/HEAD/Rectangular_VIT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"aaf5872885ebcf1c","mcp_get_code":{"code_sha256":"aaf5872885ebcf1c"}},{"arxiv_id":"2502.00379","paper":"/paper/latent-action-learning-requires-supervision","title":"Latent Action Learning Requires Supervision in the Presence of Distractors","date":"2025-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dunnolab/laom","path":"src/nn.py","file_url":"https://github.com/dunnolab/laom/blob/HEAD/src/nn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"895458a98fc89d34","mcp_get_code":{"code_sha256":"895458a98fc89d34"}},{"arxiv_id":"2412.12146","paper":"/paper/generative-modeling-and-data-augmentation-for","title":"Generative Modeling and Data Augmentation for Power System Production Simulation","date":"2024-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Becklishious/NeurIPS2024","path":"TS-Diffusion/Models/interpretable_diffusion/gaussian_diffusion.py","file_url":"https://github.com/Becklishious/NeurIPS2024/blob/HEAD/TS-Diffusion/Models/interpretable_diffusion/gaussian_diffusion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac3ebc988e160531","mcp_get_code":{"code_sha256":"ac3ebc988e160531"}},{"arxiv_id":"2406.10354","paper":"/paper/sigdiffusions-score-based-diffusion-models","title":"SigDiffusions: Score-Based Diffusion Models for Long Time Series via Log-Signature Embeddings","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Y-debug-sys/Diffusion-TS","path":"Models/interpretable_diffusion/gaussian_diffusion.py","file_url":"https://github.com/Y-debug-sys/Diffusion-TS/blob/HEAD/Models/interpretable_diffusion/gaussian_diffusion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f167bbb63f7b5ffd","mcp_get_code":{"code_sha256":"f167bbb63f7b5ffd"}},{"arxiv_id":"2403.17757","paper":"/paper/noise2noise-denoising-of-crism-hyperspectral","title":"Noise2Noise Denoising of CRISM Hyperspectral Data","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rob-platt/n2n4m","path":"n2n4m/model.py","file_url":"https://github.com/rob-platt/n2n4m/blob/HEAD/n2n4m/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1fe6ea46327af7a2","mcp_get_code":{"code_sha256":"1fe6ea46327af7a2"}},{"arxiv_id":"2402.19009","paper":"/paper/generating-reconstructing-and-representing","title":"Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guangyliu/eddpm","path":"Protein/codes/nn/models.py","file_url":"https://github.com/guangyliu/eddpm/blob/HEAD/Protein/codes/nn/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"73cd3359ae45d6c1","mcp_get_code":{"code_sha256":"73cd3359ae45d6c1"}},{"arxiv_id":"2310.10971","paper":"/paper/context-aware-meta-learning","title":"Context-Aware Meta-Learning","date":"2023-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cfifty/CAML","path":"src/models/CAML.py","file_url":"https://github.com/cfifty/CAML/blob/HEAD/src/models/CAML.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8535c1852720eb4f","mcp_get_code":{"code_sha256":"8535c1852720eb4f"}},{"arxiv_id":"2305.00664","paper":"/paper/dynamic-transfer-learning-across-graphs","title":"EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs","date":"2023-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wanghh7/evolunet","path":"model/model.py","file_url":"https://github.com/wanghh7/evolunet/blob/HEAD/model/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d9faf5d7973ad371","mcp_get_code":{"code_sha256":"d9faf5d7973ad371"}},{"arxiv_id":"2204.01696","paper":"/paper/joint-hand-motion-and-interaction-hotspots","title":"Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos","date":"2022-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stevenlsw/hoi-forecast","path":"networks/transformer.py","file_url":"https://github.com/stevenlsw/hoi-forecast/blob/HEAD/networks/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c020f10b0bec0e6","mcp_get_code":{"code_sha256":"0c020f10b0bec0e6"}},{"arxiv_id":"2203.12119","paper":"/paper/visual-prompt-tuning","title":"Visual Prompt Tuning","date":"2022-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Yiming-M/CLIP-EBC","path":"models/encoder/vit.py","file_url":"https://github.com/Yiming-M/CLIP-EBC/blob/HEAD/models/encoder/vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c6bf5366cd01303c","mcp_get_code":{"code_sha256":"c6bf5366cd01303c"}},{"arxiv_id":"2103.15619","paper":"/paper/setvae-learning-hierarchical-composition-for","title":"SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data","date":"2021-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jw9730/setvae","path":"models/networks.py","file_url":"https://github.com/jw9730/setvae/blob/HEAD/models/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d5bb50995b393f2","mcp_get_code":{"code_sha256":"6d5bb50995b393f2"}},{"arxiv_id":"2102.07108","paper":"/paper/cate-computation-aware-neural-architecture","title":"CATE: Computation-aware Neural Architecture Encoding with Transformers","date":"2021-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MSU-MLSys-Lab/CATE","path":"layers/transformer.py","file_url":"https://github.com/MSU-MLSys-Lab/CATE/blob/HEAD/layers/transformer.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":"2b2a73a9b0d3825e","mcp_get_code":{"code_sha256":"2b2a73a9b0d3825e"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pytorch/vision","path":"torchvision/models/vision_transformer.py","file_url":"https://github.com/pytorch/vision/blob/HEAD/torchvision/models/vision_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"98bf127d64416382","mcp_get_code":{"code_sha256":"98bf127d64416382"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sangHa0411/VIT","path":"model.py","file_url":"https://github.com/sangHa0411/VIT/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":"ce7cae4240dc4421","mcp_get_code":{"code_sha256":"ce7cae4240dc4421"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/ClassyVision","path":"classy_vision/models/vision_transformer.py","file_url":"https://github.com/facebookresearch/ClassyVision/blob/HEAD/classy_vision/models/vision_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5a11eb1ce59c052b","mcp_get_code":{"code_sha256":"5a11eb1ce59c052b"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KiUngSong/Vision","path":"ViT/ViT_pytorch.py","file_url":"https://github.com/KiUngSong/Vision/blob/HEAD/ViT/ViT_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0341cd2b3c276bd3","mcp_get_code":{"code_sha256":"0341cd2b3c276bd3"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"asyml/vision-transformer-pytorch","path":"src/model.py","file_url":"https://github.com/asyml/vision-transformer-pytorch/blob/HEAD/src/model.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":"be6b45c4b5087cd0","mcp_get_code":{"code_sha256":"be6b45c4b5087cd0"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bshantam97/Attention_Based_Networks","path":"vision_transformer.py","file_url":"https://github.com/bshantam97/Attention_Based_Networks/blob/HEAD/vision_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"32185c117e4e5926","mcp_get_code":{"code_sha256":"32185c117e4e5926"}},{"arxiv_id":"2010.03768","paper":"/paper/alfworld-aligning-text-and-embodied","title":"ALFWorld: Aligning Text and Embodied Environments for Interactive Learning","date":"2020-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alfworld/alfworld","path":"alfworld/agents/modules/model.py","file_url":"https://github.com/alfworld/alfworld/blob/HEAD/alfworld/agents/modules/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"72bcf63ba171e486","mcp_get_code":{"code_sha256":"72bcf63ba171e486"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bot66/mnistdiffusion","path":"model.py","file_url":"https://github.com/bot66/mnistdiffusion/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bfa427b1e754f31a","mcp_get_code":{"code_sha256":"bfa427b1e754f31a"}},{"arxiv_id":"2005.06409","paper":"/paper/dense-caption-matching-and-frame-selection","title":"Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA","date":"2020-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hyounghk/VideoQADenseCapFrameGate-ACL2020","path":"qanet/tvqanet.py","file_url":"https://github.com/hyounghk/VideoQADenseCapFrameGate-ACL2020/blob/HEAD/qanet/tvqanet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"141e2be2575e53f1","mcp_get_code":{"code_sha256":"141e2be2575e53f1"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kh-kim/simple-nmt","path":"simple_nmt/models/transformer.py","file_url":"https://github.com/kh-kim/simple-nmt/blob/HEAD/simple_nmt/models/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d62179f4dfe9da60","mcp_get_code":{"code_sha256":"d62179f4dfe9da60"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BrianPulfer/PapersReimplementations","path":"src/nlp/layers/encoder.py","file_url":"https://github.com/BrianPulfer/PapersReimplementations/blob/HEAD/src/nlp/layers/encoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"222c8ded23cb43b4","mcp_get_code":{"code_sha256":"222c8ded23cb43b4"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sarthaxxxxx/Attention-is-all-you-need","path":"Transformer.py","file_url":"https://github.com/sarthaxxxxx/Attention-is-all-you-need/blob/HEAD/Transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4a8ce345b526315d","mcp_get_code":{"code_sha256":"4a8ce345b526315d"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shao-chi/ImageCaption","path":"core/TRANSFORMER/model.py","file_url":"https://github.com/shao-chi/ImageCaption/blob/HEAD/core/TRANSFORMER/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3236b946a3b8e40f","mcp_get_code":{"code_sha256":"3236b946a3b8e40f"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"plkmo/Transformer-Eng2French","path":"src/models.py","file_url":"https://github.com/plkmo/Transformer-Eng2French/blob/HEAD/src/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"32aeb96eef5cde78","mcp_get_code":{"code_sha256":"32aeb96eef5cde78"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seunghwan1228/Transfomer-MachineTranslation","path":"models/transformer.py","file_url":"https://github.com/seunghwan1228/Transfomer-MachineTranslation/blob/HEAD/models/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6adcddb6bc8bd882","mcp_get_code":{"code_sha256":"6adcddb6bc8bd882"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"park-cheol/Speech_Enhancement-DCUnet","path":"model/DCUNet.py","file_url":"https://github.com/park-cheol/Speech_Enhancement-DCUnet/blob/HEAD/model/DCUNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2966075927b298e4","mcp_get_code":{"code_sha256":"2966075927b298e4"}}]}