{"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/top-k","entry":"top_k","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":17,"n_papers_ran":17,"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":8,"n_places":17,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":6,"ran_fixture":1,"ran":2,"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":"2407.09099","paper":"/paper/music-proofreading-with-refinpaint-where-and","title":"Music Proofreading with RefinPaint: Where and How to Modify Compositions given Context","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ta603/refinpaint","path":"InpaintingModel.py","file_url":"https://github.com/ta603/refinpaint/blob/HEAD/InpaintingModel.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":"39f3cec3c6be459f","mcp_get_code":{"code_sha256":"39f3cec3c6be459f"}},{"arxiv_id":"2406.18051","paper":"/paper/vit-1-58b-mobile-vision-transformers-in-the-1","title":"ViT-1.58b: Mobile Vision Transformers in the 1-bit Era","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dlyuangod/vit-1.58b","path":"BitNet/bitnet/at.py","file_url":"https://github.com/dlyuangod/vit-1.58b/blob/HEAD/BitNet/bitnet/at.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":"3b65d5d77510c964","mcp_get_code":{"code_sha256":"3b65d5d77510c964"}},{"arxiv_id":"2406.07368","paper":"/paper/when-linear-attention-meets-autoregressive","title":"When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models","date":"2024-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gatech-eic/linearized-llm","path":"autoregressive_wrapper.py","file_url":"https://github.com/gatech-eic/linearized-llm/blob/HEAD/autoregressive_wrapper.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5d77a7df8b8103e4","mcp_get_code":{"code_sha256":"5d77a7df8b8103e4"}},{"arxiv_id":"2309.07822","paper":"/paper/catfood-counterfactual-augmented-training-for","title":"CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration","date":"2023-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ukplab/catfood","path":"src/shortcuts/eval_metrics.py","file_url":"https://github.com/ukplab/catfood/blob/HEAD/src/shortcuts/eval_metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7eabcc356bd276af","mcp_get_code":{"code_sha256":"7eabcc356bd276af"}},{"arxiv_id":"2307.10037","paper":"/paper/single-cell-rna-seq-data-imputation-using","title":"Single-cell RNA-seq data imputation using Feature Propagation","date":null,"month_inferred_from_arxiv_id":"2023-07","title_source":"archive","repo":"Junseok0207/scFP","path":"misc/graph_construction.py","file_url":"https://github.com/Junseok0207/scFP/blob/HEAD/misc/graph_construction.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"62ada701da1eed6d","mcp_get_code":{"code_sha256":"62ada701da1eed6d"}},{"arxiv_id":"2212.11134","paper":"/paper/generating-music-with-sentiment-using","title":"Generating music with sentiment using Transformer-GANs","date":"2022-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pneves1051/transformers_sentiment","path":"utils/generate.py","file_url":"https://github.com/pneves1051/transformers_sentiment/blob/HEAD/utils/generate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"72827010274936db","mcp_get_code":{"code_sha256":"72827010274936db"}},{"arxiv_id":"2212.10368","paper":"/paper/masked-event-modeling-self-supervised","title":"Masked Event Modeling: Self-Supervised Pretraining for Event Cameras","date":"2022-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tum-vision/mem","path":"mem/modeling_discrete_vae.py","file_url":"https://github.com/tum-vision/mem/blob/HEAD/mem/modeling_discrete_vae.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fc2efb619b6ee2c4","mcp_get_code":{"code_sha256":"fc2efb619b6ee2c4"}},{"arxiv_id":"2211.17192","paper":"/paper/fast-inference-from-transformers-via","title":"Fast Inference from Transformers via Speculative Decoding","date":"2022-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/speculative-decoding","path":"speculative_decoding/speculative_decoding.py","file_url":"https://github.com/lucidrains/speculative-decoding/blob/HEAD/speculative_decoding/speculative_decoding.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":"3c61d89a5e42a3d3","mcp_get_code":{"code_sha256":"3c61d89a5e42a3d3"}},{"arxiv_id":"2210.16484","paper":"/paper/a-systematic-survey-of-molecular-pre-trained","title":"A Systematic Survey of Chemical Pre-trained Models","date":"2022-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junxia97/mole-bert","path":"vqvae.py","file_url":"https://github.com/junxia97/mole-bert/blob/HEAD/vqvae.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fc2efb619b6ee2c4","mcp_get_code":{"code_sha256":"fc2efb619b6ee2c4"}},{"arxiv_id":"2203.07852","paper":"/paper/block-recurrent-transformers","title":"Block-Recurrent Transformers","date":"2022-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/block-recurrent-transformer-pytorch","path":"block_recurrent_transformer_pytorch/block_recurrent_transformer_pytorch.py","file_url":"https://github.com/lucidrains/block-recurrent-transformer-pytorch/blob/HEAD/block_recurrent_transformer_pytorch/block_recurrent_transformer_pytorch.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":"39f3cec3c6be459f","mcp_get_code":{"code_sha256":"39f3cec3c6be459f"}},{"arxiv_id":"2201.08239","paper":"/paper/lamda-language-models-for-dialog-applications","title":"LaMDA: Language Models for Dialog Applications","date":"2022-01-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"conceptofmind/lamda-rlhf-pytorch","path":"lamda_pytorch/utils/utils.py","file_url":"https://github.com/conceptofmind/lamda-rlhf-pytorch/blob/HEAD/lamda_pytorch/utils/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":"3b65d5d77510c964","mcp_get_code":{"code_sha256":"3b65d5d77510c964"}},{"arxiv_id":"2112.05682","paper":"/paper/self-attention-does-not-need-o-n-2-memory","title":"Self-attention Does Not Need $O(n^2)$ Memory","date":"2021-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/memory-efficient-attention-pytorch","path":"memory_efficient_attention_pytorch/autoregressive_wrapper.py","file_url":"https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/HEAD/memory_efficient_attention_pytorch/autoregressive_wrapper.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":"3b65d5d77510c964","mcp_get_code":{"code_sha256":"3b65d5d77510c964"}},{"arxiv_id":"2107.11906","paper":"/paper/h-transformer-1d-fast-one-dimensional","title":"H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences","date":"2021-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/h-transformer-1d","path":"h_transformer_1d/autoregressive_wrapper.py","file_url":"https://github.com/lucidrains/h-transformer-1d/blob/HEAD/h_transformer_1d/autoregressive_wrapper.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":"3b65d5d77510c964","mcp_get_code":{"code_sha256":"3b65d5d77510c964"}},{"arxiv_id":"2006.15020","paper":"/paper/pre-training-via-paraphrasing","title":"Pre-training via Paraphrasing","date":"2020-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/marge-pytorch","path":"marge_pytorch/marge_pytorch.py","file_url":"https://github.com/lucidrains/marge-pytorch/blob/HEAD/marge_pytorch/marge_pytorch.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":"3b65d5d77510c964","mcp_get_code":{"code_sha256":"3b65d5d77510c964"}},{"arxiv_id":"1812.01243","paper":"/paper/factorized-attention-self-attention-with","title":"Efficient Attention: Attention with Linear Complexities","date":"2018-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/memory-transformer-xl","path":"memory_transformer_xl/autoregressive_wrapper.py","file_url":"https://github.com/lucidrains/memory-transformer-xl/blob/HEAD/memory_transformer_xl/autoregressive_wrapper.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":"3b65d5d77510c964","mcp_get_code":{"code_sha256":"3b65d5d77510c964"}},{"arxiv_id":"1804.09170","paper":"/paper/realistic-evaluation-of-deep-semi-supervised","title":"Realistic Evaluation of Deep Semi-Supervised Learning Algorithms","date":"2018-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brain-research/realistic-ssl-evaluation","path":"evaluate_model.py","file_url":"https://github.com/brain-research/realistic-ssl-evaluation/blob/HEAD/evaluate_model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4c46154099e4d408","mcp_get_code":{"code_sha256":"4c46154099e4d408"}},{"arxiv_id":"aaai_25604","paper":null,"title":"arXiv:aaai_25604","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"xcyao00/PMAD","path":"dall_e/tokenizer.py","file_url":"https://github.com/xcyao00/PMAD/blob/HEAD/dall_e/tokenizer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc2efb619b6ee2c4","mcp_get_code":{"code_sha256":"fc2efb619b6ee2c4"}}]}