{"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/orthogonal-matrix-chunk","entry":"orthogonal_matrix_chunk","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":4,"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":5,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":3,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":2},"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":"2410.15500","paper":"/paper/anonymising-elderly-and-pathological-speech","title":"Anonymising Elderly and Pathological Speech: Voice Conversion Using DDSP and Query-by-Example","date":"2024-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"suhitaghosh10/ddsp-qbe","path":"fusion_synthesis/ddsp/conformer.py","file_url":"https://github.com/suhitaghosh10/ddsp-qbe/blob/HEAD/fusion_synthesis/ddsp/conformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a8cffaf25442a3ed","mcp_get_code":{"code_sha256":"a8cffaf25442a3ed"}},{"arxiv_id":"2310.13225","paper":"/paper/scalable-neural-network-kernels","title":"Scalable Neural Network Kernels","date":"2023-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arijitthegame/neural-network-kernels","path":"src/nnk/nnk.py","file_url":"https://github.com/arijitthegame/neural-network-kernels/blob/HEAD/src/nnk/nnk.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"97b8b7b080cf8316","mcp_get_code":{"code_sha256":"97b8b7b080cf8316"}},{"arxiv_id":"2307.10802","paper":"/paper/meta-transformer-a-unified-framework-for","title":"Meta-Transformer: A Unified Framework for Multimodal Learning","date":"2023-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"invictus717/MetaTransformer","path":"Graph/metatransformer/modules/orf.py","file_url":"https://github.com/invictus717/MetaTransformer/blob/HEAD/Graph/metatransformer/modules/orf.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":"a02dd771934e8662","mcp_get_code":{"code_sha256":"a02dd771934e8662"}},{"arxiv_id":"2207.02505","paper":"/paper/pure-transformers-are-powerful-graph-learners","title":"Pure Transformers are Powerful Graph Learners","date":"2022-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jw9730/tokengt","path":"large-scale-regression/tokengt/modules/orf.py","file_url":"https://github.com/jw9730/tokengt/blob/HEAD/large-scale-regression/tokengt/modules/orf.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a02dd771934e8662","mcp_get_code":{"code_sha256":"a02dd771934e8662"}},{"arxiv_id":"2201.03794","paper":"/paper/efficient-non-local-contrastive-attention-for","title":"Efficient Non-Local Contrastive Attention for Image Super-Resolution","date":"2022-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zj-binxia/enlca","path":"src/model/ENLA.py","file_url":"https://github.com/zj-binxia/enlca/blob/HEAD/src/model/ENLA.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b2edd96923818916","mcp_get_code":{"code_sha256":"b2edd96923818916"}},{"arxiv_id":"2106.06295","paper":"/paper/going-beyond-linear-transformers-with","title":"Going Beyond Linear Transformers with Recurrent Fast Weight Programmers","date":"2021-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IDSIA/lmtool-fwms","path":"src/utils/performer_helper.py","file_url":"https://github.com/IDSIA/lmtool-fwms/blob/HEAD/src/utils/performer_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":"aa14b5ef69b1c4ef","mcp_get_code":{"code_sha256":"aa14b5ef69b1c4ef"}}]}