{"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/decodermodel","entry":"DecoderModel","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":5,"n_papers_ran":0,"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":6,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":6},"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":"2505.12167","paper":"/paper/fable-a-localized-targeted-adversarial-attack","title":"FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models","date":"2025-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EDAPINENUT/CLCRN","path":"model/clcnn/recurrent/seq2seq_model.py","file_url":"https://github.com/EDAPINENUT/CLCRN/blob/HEAD/model/clcnn/recurrent/seq2seq_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"732a31024b7e5639","mcp_get_code":{"code_sha256":"732a31024b7e5639"}},{"arxiv_id":"2303.17959","paper":"/paper/diffusion-action-segmentation","title":"Diffusion Action Segmentation","date":"2023-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"finspire13/diffact","path":"model.py","file_url":"https://github.com/finspire13/diffact/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d5e99d7e89351b01","mcp_get_code":{"code_sha256":"d5e99d7e89351b01"}},{"arxiv_id":"2101.06861","paper":"/paper/discrete-graph-structure-learning-for-1","title":"Discrete Graph Structure Learning for Forecasting Multiple Time Series","date":"2021-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaoshangcs/GTS","path":"model/pytorch/model.py","file_url":"https://github.com/chaoshangcs/GTS/blob/HEAD/model/pytorch/model.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":"7d513ce42a291ea3","mcp_get_code":{"code_sha256":"7d513ce42a291ea3"}},{"arxiv_id":"2010.16056","paper":"/paper/generating-radiology-reports-via-memory","title":"Generating Radiology Reports via Memory-driven Transformer","date":"2020-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jbdel/vilmedic","path":"vilmedic/models/rrg/RRG.py","file_url":"https://github.com/jbdel/vilmedic/blob/HEAD/vilmedic/models/rrg/RRG.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f582c3015724586c","mcp_get_code":{"code_sha256":"f582c3015724586c"}},{"arxiv_id":"1707.01926","paper":"/paper/diffusion-convolutional-recurrent-neural","title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting","date":"2017-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tijsmaas/TrafficPrediction","path":"model/pytorch/dcrnn_model.py","file_url":"https://github.com/tijsmaas/TrafficPrediction/blob/HEAD/model/pytorch/dcrnn_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4ff718f4bfb8cb58","mcp_get_code":{"code_sha256":"4ff718f4bfb8cb58"}},{"arxiv_id":"1707.01926","paper":"/paper/diffusion-convolutional-recurrent-neural","title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting","date":"2017-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chnsh/DCRNN_PyTorch","path":"model/pytorch/dcrnn_model.py","file_url":"https://github.com/chnsh/DCRNN_PyTorch/blob/HEAD/model/pytorch/dcrnn_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac5645e8e8afed2e","mcp_get_code":{"code_sha256":"ac5645e8e8afed2e"}}]}