{"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/interleave","entry":"interleave","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":9,"n_papers_ran":5,"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":8,"n_samples_ran":4,"n_samples_fingerprinted":2,"n_places":10,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":1,"ran":0,"unverified":4},"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":"2510.03993","paper":"/paper/arxiv-2510-03993","title":"Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"yaxinhou/CPG","path":"semilearn/algorithms/utils/ops.py","file_url":"https://github.com/yaxinhou/CPG/blob/HEAD/semilearn/algorithms/utils/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c56b7c3374c4b9cc","mcp_get_code":{"code_sha256":"c56b7c3374c4b9cc"}},{"arxiv_id":"2405.11756","paper":"/paper/erasing-the-bias-fine-tuning-foundation","title":"Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gank0078/FineSSL","path":"trainer.py","file_url":"https://github.com/Gank0078/FineSSL/blob/HEAD/trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f6043e7798bcabbd","mcp_get_code":{"code_sha256":"f6043e7798bcabbd"}},{"arxiv_id":"2404.11003","paper":"/paper/infomatch-entropy-neural-estimation-for-semi","title":"InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification","date":"2024-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kunzhan/InfoMatch","path":"infoMatch_STL.py","file_url":"https://github.com/kunzhan/InfoMatch/blob/HEAD/infoMatch_STL.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f6043e7798bcabbd","mcp_get_code":{"code_sha256":"f6043e7798bcabbd"}},{"arxiv_id":"2308.14575","paper":"/paper/referring-image-segmentation-using-text","title":"Referring Image Segmentation Using Text Supervision","date":"2023-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fawnliu/tris","path":"IRNet/torch_utils.py","file_url":"https://github.com/fawnliu/tris/blob/HEAD/IRNet/torch_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f6043e7798bcabbd","mcp_get_code":{"code_sha256":"f6043e7798bcabbd"}},{"arxiv_id":"1911.06393","paper":"/paper/seq-u-net-a-one-dimensional-causal-u-net-for","title":"Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling","date":"2019-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"f90/Seq-U-Net","path":"sequnet_utils.py","file_url":"https://github.com/f90/Seq-U-Net/blob/HEAD/sequnet_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0356ec7f2f97ed82","mcp_get_code":{"code_sha256":"0356ec7f2f97ed82"}},{"arxiv_id":"1905.02249","paper":"/paper/mixmatch-a-holistic-approach-to-semi","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","date":"2019-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smkim7-kr/albu-MixMatch-pytorch","path":"src/utils.py","file_url":"https://github.com/smkim7-kr/albu-MixMatch-pytorch/blob/HEAD/src/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"87035ef75e16edc6","mcp_get_code":{"code_sha256":"87035ef75e16edc6"}},{"arxiv_id":"1905.02249","paper":"/paper/mixmatch-a-holistic-approach-to-semi","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","date":"2019-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ntozer/mixmatch-tensorflow2.0","path":"mixmatch.py","file_url":"https://github.com/ntozer/mixmatch-tensorflow2.0/blob/HEAD/mixmatch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc463911b8db674e","mcp_get_code":{"code_sha256":"dc463911b8db674e"}},{"arxiv_id":"1903.12087","paper":"/paper/a-real-time-wideband-neural-vocoder-at-16-kbs","title":"A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet","date":"2019-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mozilla/LPCNet","path":"training_tf2/lpcnet.py","file_url":"https://github.com/mozilla/LPCNet/blob/HEAD/training_tf2/lpcnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"462214bb0c5c4921","mcp_get_code":{"code_sha256":"462214bb0c5c4921"}},{"arxiv_id":"1804.02747","paper":"/paper/fast-conditional-independence-test-for-vector","title":"Fast Conditional Independence Test for Vector Variables with Large Sample Sizes","date":"2018-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kjchalup/fcit","path":"fcit/fcit.py","file_url":"https://github.com/kjchalup/fcit/blob/HEAD/fcit/fcit.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"28c086173a0a9308","mcp_get_code":{"code_sha256":"28c086173a0a9308"}},{"arxiv_id":"1606.00373","paper":"/paper/deeper-depth-prediction-with-fully","title":"Deeper Depth Prediction with Fully Convolutional Residual Networks","date":"2016-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iro-cp/FCRN-DepthPrediction","path":"tensorflow/models/network.py","file_url":"https://github.com/iro-cp/FCRN-DepthPrediction/blob/HEAD/tensorflow/models/network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"95ceecbd3deb30dc","mcp_get_code":{"code_sha256":"95ceecbd3deb30dc"}}]}