{"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/fixedembedding","entry":"FixedEmbedding","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":6,"n_papers_ran":6,"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":9,"n_samples_ran":9,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":8,"unverified":0},"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":"2605.24523","paper":"/paper/arxiv-2605-24523","title":"MindAlign: Bridging EEG, Vision, and Language for Zero-Shot Visual Decoding","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"anon-eeg/eeg_image_decoding","path":"eeg_encoders.py","file_url":"https://github.com/anon-eeg/eeg_image_decoding/blob/HEAD/eeg_encoders.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"05e26630554bd79d","mcp_get_code":{"code_sha256":"05e26630554bd79d"}},{"arxiv_id":"2403.00131","paper":"/paper/units-building-a-unified-time-series-model","title":"UniTS: A Unified Multi-Task Time Series Model","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuml/Time-Series-Library","path":"models/TimeMixer.py","file_url":"https://github.com/thuml/Time-Series-Library/blob/HEAD/models/TimeMixer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"921d4aae41f22f16","mcp_get_code":{"code_sha256":"921d4aae41f22f16"}},{"arxiv_id":"2212.08151","paper":"/paper/first-de-trend-then-attend-rethinking","title":"First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting","date":"2022-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BeBeYourLove/TDformer","path":"model/TDformer.py","file_url":"https://github.com/BeBeYourLove/TDformer/blob/HEAD/model/TDformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"16e89fafa4d72549","mcp_get_code":{"code_sha256":"16e89fafa4d72549"}},{"arxiv_id":"2012.07436","paper":"/paper/informer-beyond-efficient-transformer-for","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","date":"2020-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"martinwhl/Informer-PyTorch-Lightning","path":"models/informer/model.py","file_url":"https://github.com/martinwhl/Informer-PyTorch-Lightning/blob/HEAD/models/informer/model.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":"11d00cdc1be5dc46","mcp_get_code":{"code_sha256":"11d00cdc1be5dc46"}},{"arxiv_id":"2012.07436","paper":"/paper/informer-beyond-efficient-transformer-for","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","date":"2020-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AndrzejMiskow/TradeAI","path":"prediction_service/transformers/models.py","file_url":"https://github.com/AndrzejMiskow/TradeAI/blob/HEAD/prediction_service/transformers/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d54ef7cb9071ad5d","mcp_get_code":{"code_sha256":"d54ef7cb9071ad5d"}},{"arxiv_id":"2012.07436","paper":"/paper/informer-beyond-efficient-transformer-for","title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","date":"2020-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianhai123/Informer-Tensorflow","path":"models/model.py","file_url":"https://github.com/tianhai123/Informer-Tensorflow/blob/HEAD/models/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":"0545de0df17c8182","mcp_get_code":{"code_sha256":"0545de0df17c8182"}},{"arxiv_id":"2005.12872","paper":"/paper/end-to-end-object-detection-with-transformers","title":"End-to-End Object Detection with Transformers","date":"2020-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Leonardo-Blanger/detr_tensorflow","path":"detr_tensorflow/models/detr.py","file_url":"https://github.com/Leonardo-Blanger/detr_tensorflow/blob/HEAD/detr_tensorflow/models/detr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec7f81ffdbd55a8d","mcp_get_code":{"code_sha256":"ec7f81ffdbd55a8d"}},{"arxiv_id":"2005.12872","paper":"/paper/end-to-end-object-detection-with-transformers","title":"End-to-End Object Detection with Transformers","date":"2020-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Visual-Behavior/detr-tensorflow","path":"detr_tf/networks/detr.py","file_url":"https://github.com/Visual-Behavior/detr-tensorflow/blob/HEAD/detr_tf/networks/detr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14483127db755736","mcp_get_code":{"code_sha256":"14483127db755736"}},{"arxiv_id":"2004.06748","paper":"/paper/balancing-training-for-multilingual-neural","title":"Balancing Training for Multilingual Neural Machine Translation","date":"2020-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cindyxinyiwang/multiDDS","path":"fairseq/models/data_actor.py","file_url":"https://github.com/cindyxinyiwang/multiDDS/blob/HEAD/fairseq/models/data_actor.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":"863607ebaf5ac594","mcp_get_code":{"code_sha256":"863607ebaf5ac594"}}]}