{"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/make-data-loader","entry":"make_data_loader","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":13,"n_papers_ran":2,"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":11,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":14,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":1,"unverified":9},"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":"2602.00534","paper":"/paper/arxiv-2602-00534","title":"AIRE-Prune: Asymptotic Impulse-Response Energy for State Pruning in State Space Models","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"falcon-arrow/AIRE-Prune","path":"s5/dataloading.py","file_url":"https://github.com/falcon-arrow/AIRE-Prune/blob/HEAD/s5/dataloading.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":"508a42e36707cd15","mcp_get_code":{"code_sha256":"508a42e36707cd15"}},{"arxiv_id":"2411.02824","paper":"/paper/layer-adaptive-state-pruning-for-deep-state","title":"Layer-Adaptive State Pruning for Deep State Space Models","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"msgwak/LAST","path":"s5/dataloading.py","file_url":"https://github.com/msgwak/LAST/blob/HEAD/s5/dataloading.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"508a42e36707cd15","mcp_get_code":{"code_sha256":"508a42e36707cd15"}},{"arxiv_id":"2410.23227","paper":"/paper/fl-2-overcoming-few-labels-in-federated-semi","title":"(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seungjoo-ai/FLFL-NeurIPS24","path":"src/algorithm/flfl.py","file_url":"https://github.com/seungjoo-ai/FLFL-NeurIPS24/blob/HEAD/src/algorithm/flfl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e5e0e1a3245fac2","mcp_get_code":{"code_sha256":"3e5e0e1a3245fac2"}},{"arxiv_id":"2407.21448","paper":"/paper/accelerating-image-super-resolution-networks","title":"Accelerating Image Super-Resolution Networks with Pixel-Level Classification","date":"2024-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"3587jjh/PCSR","path":"train_pcsr.py","file_url":"https://github.com/3587jjh/PCSR/blob/HEAD/train_pcsr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d9e995544e2536f3","mcp_get_code":{"code_sha256":"d9e995544e2536f3"}},{"arxiv_id":"2405.19789","paper":"/paper/estimating-before-debiasing-a-bayesian","title":"Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GuogangZhu/FedDB","path":"src/modules/modules.py","file_url":"https://github.com/GuogangZhu/FedDB/blob/HEAD/src/modules/modules.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":"b9594d9b2fb26b1d","mcp_get_code":{"code_sha256":"b9594d9b2fb26b1d"}},{"arxiv_id":"2404.02257","paper":"/paper/snag-scalable-and-accurate-video-grounding","title":"SnAG: Scalable and Accurate Video Grounding","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"happyharrycn/actionformer_release","path":"libs/datasets/datasets.py","file_url":"https://github.com/happyharrycn/actionformer_release/blob/HEAD/libs/datasets/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"635369f63ec96b89","mcp_get_code":{"code_sha256":"635369f63ec96b89"}},{"arxiv_id":"2402.15584","paper":"/paper/state-space-models-for-event-cameras","title":"State Space Models for Event Cameras","date":"2024-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lindermanlab/S5","path":"s5/dataloading.py","file_url":"https://github.com/lindermanlab/S5/blob/HEAD/s5/dataloading.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"508a42e36707cd15","mcp_get_code":{"code_sha256":"508a42e36707cd15"}},{"arxiv_id":"2307.05695","paper":"/paper/stack-more-layers-differently-high-rank","title":"ReLoRA: High-Rank Training Through Low-Rank Updates","date":"2023-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guitaricet/peft_pretraining","path":"peft_pretraining/megatron_dataset/dataloader.py","file_url":"https://github.com/guitaricet/peft_pretraining/blob/HEAD/peft_pretraining/megatron_dataset/dataloader.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":"d49462681436c44b","mcp_get_code":{"code_sha256":"d49462681436c44b"}},{"arxiv_id":"2303.07347","paper":"/paper/tridet-temporal-action-detection-with","title":"TriDet: Temporal Action Detection with Relative Boundary Modeling","date":"2023-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingfengshi/TriDet","path":"libs/datasets/datasets.py","file_url":"https://github.com/dingfengshi/TriDet/blob/HEAD/libs/datasets/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"635369f63ec96b89","mcp_get_code":{"code_sha256":"635369f63ec96b89"}},{"arxiv_id":"2207.01573","paper":"/paper/embedding-contrastive-unsupervised-features","title":"Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets","date":"2022-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PaulAlbert31/SNCF","path":"main_unsup.py","file_url":"https://github.com/PaulAlbert31/SNCF/blob/HEAD/main_unsup.py","status":"ran_fixture","verification_level":1,"contract_check":"DEP_MISSING","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0cb6c4fb7dcd4ceb","mcp_get_code":{"code_sha256":"0cb6c4fb7dcd4ceb"}},{"arxiv_id":"2111.00195","paper":"/paper/learning-continuous-representation-of-audio","title":"Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution","date":"2021-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-postech/lisa","path":"eval_lisa.py","file_url":"https://github.com/ml-postech/lisa/blob/HEAD/eval_lisa.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":"2b9206b01b141fc0","mcp_get_code":{"code_sha256":"2b9206b01b141fc0"}},{"arxiv_id":"2111.00195","paper":"/paper/learning-continuous-representation-of-audio","title":"Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution","date":"2021-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-postech/lisa","path":"train_lisa.py","file_url":"https://github.com/ml-postech/lisa/blob/HEAD/train_lisa.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":"f76f573e26dc1218","mcp_get_code":{"code_sha256":"f76f573e26dc1218"}},{"arxiv_id":"2007.14628","paper":"/paper/solving-the-blind-perspective-n-point-problem","title":"Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization","date":"2020-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Liumouliu/Deep_blind_PnP","path":"lib/data_loaders.py","file_url":"https://github.com/Liumouliu/Deep_blind_PnP/blob/HEAD/lib/data_loaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad9e933f883a8298","mcp_get_code":{"code_sha256":"ad9e933f883a8298"}},{"arxiv_id":"Xiao_Towards_Progressive_Multi-Frequency_Representation_for_Image_Warping_CVPR_2024_paper","paper":null,"title":"arXiv:Xiao_Towards_Progressive_Multi-Frequency_Representation_for_Image_Warping_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"junxiao01/MFR","path":"train_sr.py","file_url":"https://github.com/junxiao01/MFR/blob/HEAD/train_sr.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":"1d7b5ecb7b006bb7","mcp_get_code":{"code_sha256":"1d7b5ecb7b006bb7"}}]}