{"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/train-one-epoch","entry":"train_one_epoch","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":50,"n_papers_ran":16,"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":52,"n_samples_ran":16,"n_samples_fingerprinted":0,"n_places":55,"n_places_pointer_only":17,"by_status":{"ran_honours":3,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":2,"ran":11,"unverified":36},"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":"2607.14509","paper":"/paper/arxiv-2607-14509","title":"Multi-Scale ViT Inference with Habitat-Fit Priors and kNN Retrieval for Multi-Species Plant Identification","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"dsgt-arc/plantclef-2026","path":"user/alper/src/Exp-10_ConvNext_finetuning/train_convnext.py","file_url":"https://github.com/dsgt-arc/plantclef-2026/blob/HEAD/user/alper/src/Exp-10_ConvNext_finetuning/train_convnext.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a0dcf6fbecebeed","mcp_get_code":{"code_sha256":"6a0dcf6fbecebeed"}},{"arxiv_id":"2607.02447","paper":"/paper/arxiv-2607-02447","title":"Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"xuanyuLawrence/FedMAR-DecMAR","path":"engine_pretrain.py","file_url":"https://github.com/xuanyuLawrence/FedMAR-DecMAR/blob/HEAD/engine_pretrain.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cd040a85fd95a590","mcp_get_code":{"code_sha256":"cd040a85fd95a590"}},{"arxiv_id":"2606.19374","paper":"/paper/arxiv-2606-19374","title":"Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"mohamedmohamed2021/SSProNet","path":"run_ProNet_LBA.py","file_url":"https://github.com/mohamedmohamed2021/SSProNet/blob/HEAD/run_ProNet_LBA.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8d9988c89af51889","mcp_get_code":{"code_sha256":"8d9988c89af51889"}},{"arxiv_id":"2606.04971","paper":"/paper/arxiv-2606-04971","title":"Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"anna-richter/be-fair","path":"aide/logs/14-addition_2/best_solution.py","file_url":"https://github.com/anna-richter/be-fair/blob/HEAD/aide/logs/14-addition_2/best_solution.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":"ea77f6a7f0ecdc2a","mcp_get_code":{"code_sha256":"ea77f6a7f0ecdc2a"}},{"arxiv_id":"2605.26283","paper":"/paper/arxiv-2605-26283","title":"Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Durjoy001/Retinal-NeuralNET","path":"misc/train_vit.py","file_url":"https://github.com/Durjoy001/Retinal-NeuralNET/blob/HEAD/misc/train_vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"adfe9e91da21b6e1","mcp_get_code":{"code_sha256":"adfe9e91da21b6e1"}},{"arxiv_id":"2605.24687","paper":"/paper/arxiv-2605-24687","title":"HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasing","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"1059684669/HoloFair","path":"classifiers/train-age.py","file_url":"https://github.com/1059684669/HoloFair/blob/HEAD/classifiers/train-age.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":"14ea69da141744a8","mcp_get_code":{"code_sha256":"14ea69da141744a8"}},{"arxiv_id":"2605.24687","paper":"/paper/arxiv-2605-24687","title":"HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasing","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"1059684669/HoloFair","path":"classifiers/train_gender.py","file_url":"https://github.com/1059684669/HoloFair/blob/HEAD/classifiers/train_gender.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":"d17dfc9cd07ef0b6","mcp_get_code":{"code_sha256":"d17dfc9cd07ef0b6"}},{"arxiv_id":"2605.24687","paper":"/paper/arxiv-2605-24687","title":"HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasing","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"1059684669/HoloFair","path":"classifiers/train_race.py","file_url":"https://github.com/1059684669/HoloFair/blob/HEAD/classifiers/train_race.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":"2bffa962b6201299","mcp_get_code":{"code_sha256":"2bffa962b6201299"}},{"arxiv_id":"2605.01625","paper":"/paper/arxiv-2605-01625","title":"PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"HySonLab/PRIME","path":"train_prime.py","file_url":"https://github.com/HySonLab/PRIME/blob/HEAD/train_prime.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"72d1a5a69354f146","mcp_get_code":{"code_sha256":"72d1a5a69354f146"}},{"arxiv_id":"2604.11374","paper":"/paper/arxiv-2604-11374","title":"What Do Vision-Language Models Encode for Personalized Image Aesthetics Assessment?","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"ynklab/vlm-latent-piaa","path":"ici/phase1_train_resnet.py","file_url":"https://github.com/ynklab/vlm-latent-piaa/blob/HEAD/ici/phase1_train_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"93fea3a2ef024e78","mcp_get_code":{"code_sha256":"93fea3a2ef024e78"}},{"arxiv_id":"2602.18406","paper":"/paper/arxiv-2602-18406","title":"GRaM workshop at ICLR 2026 Tiny Paper Track LATENT EQUIVARIANT OPERATORS FOR ROBUST OBJECT RECOGNITION: PROMISES AND CHALLENGES","date":"2026-02-20","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"BRAIN-Aalto/equivariant_operator","path":"training/base.py","file_url":"https://github.com/BRAIN-Aalto/equivariant_operator/blob/HEAD/training/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"90d7d7e5feaadf88","mcp_get_code":{"code_sha256":"90d7d7e5feaadf88"}},{"arxiv_id":"2509.12019","paper":"/paper/arxiv-2509-12019","title":"AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"dlwns147/amq","path":"amq/predictor/mlp.py","file_url":"https://github.com/dlwns147/amq/blob/HEAD/amq/predictor/mlp.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":"5afc3b858c2cc197","mcp_get_code":{"code_sha256":"5afc3b858c2cc197"}},{"arxiv_id":"2508.15868","paper":"/paper/arxiv-2508-15868","title":"CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"WNQzhu/CARFT","path":"code/mwp_ReFT/train_reward_model.py","file_url":"https://github.com/WNQzhu/CARFT/blob/HEAD/code/mwp_ReFT/train_reward_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d3b8681876990bfa","mcp_get_code":{"code_sha256":"d3b8681876990bfa"}},{"arxiv_id":"2506.03355","paper":"/paper/robustness-in-both-domains-clip-needs-a","title":"Robustness in Both Domains: CLIP Needs a Robust Text Encoder","date":"2025-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LIONS-EPFL/LEAF","path":"src/robust_vlm/train/adversarial_training_clip.py","file_url":"https://github.com/LIONS-EPFL/LEAF/blob/HEAD/src/robust_vlm/train/adversarial_training_clip.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bca77f125ea3f056","mcp_get_code":{"code_sha256":"bca77f125ea3f056"}},{"arxiv_id":"2505.04608","paper":"/paper/watch-weighted-adaptive-testing-for","title":"WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales","date":"2025-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aaronhan223/watch","path":"src/main_mnist_cifar.py","file_url":"https://github.com/aaronhan223/watch/blob/HEAD/src/main_mnist_cifar.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":"e224e4494d78ef17","mcp_get_code":{"code_sha256":"e224e4494d78ef17"}},{"arxiv_id":"2412.03906","paper":"/paper/final-model-only-data-attribution-with-a","title":"Final-Model-Only Data Attribution with a Unifying View of Gradient-Based Methods","date":"2024-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/fimoda","path":"fimoda/tabular/training.py","file_url":"https://github.com/IBM/fimoda/blob/HEAD/fimoda/tabular/training.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":"8e71326007bbf880","mcp_get_code":{"code_sha256":"8e71326007bbf880"}},{"arxiv_id":"2412.03906","paper":"/paper/final-model-only-data-attribution-with-a","title":"Final-Model-Only Data Attribution with a Unifying View of Gradient-Based Methods","date":"2024-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/fimoda","path":"fimoda/text/training_LM.py","file_url":"https://github.com/IBM/fimoda/blob/HEAD/fimoda/text/training_LM.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":"c1d765a743afa60f","mcp_get_code":{"code_sha256":"c1d765a743afa60f"}},{"arxiv_id":"2411.02796","paper":"/paper/specialized-foundation-models-struggle-to","title":"Specialized Foundation Models Struggle to Beat Supervised Baselines","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ritvikgupta199/DASHA","path":"src/main_dasha.py","file_url":"https://github.com/ritvikgupta199/DASHA/blob/HEAD/src/main_dasha.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a98c2f3b85e8e917","mcp_get_code":{"code_sha256":"a98c2f3b85e8e917"}},{"arxiv_id":"2410.19816","paper":"/paper/divshift-exploring-domain-specific","title":"DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"moiexpositoalonsolab/DivShift","path":"src/supervised_train.py","file_url":"https://github.com/moiexpositoalonsolab/DivShift/blob/HEAD/src/supervised_train.py","status":"ran_fixture","verification_level":1,"contract_check":"TIMEOUT","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cfc9059e2d6da587","mcp_get_code":{"code_sha256":"cfc9059e2d6da587"}},{"arxiv_id":"2409.11772","paper":"/paper/symmetry-based-structured-matrices-for","title":"Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks","date":"2024-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kiryteo/gm-cnn","path":"runner.py","file_url":"https://github.com/kiryteo/gm-cnn/blob/HEAD/runner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cb0f546b0a75c022","mcp_get_code":{"code_sha256":"cb0f546b0a75c022"}},{"arxiv_id":"2407.04616","paper":"/paper/isomorphic-pruning-for-vision-models","title":"Isomorphic Pruning for Vision Models","date":"2024-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VainF/Isomorphic-Pruning","path":"convnext_train.py","file_url":"https://github.com/VainF/Isomorphic-Pruning/blob/HEAD/convnext_train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"83f2bf35d7803cda","mcp_get_code":{"code_sha256":"83f2bf35d7803cda"}},{"arxiv_id":"2405.19707","paper":"/paper/demamba-ai-generated-video-detection-on","title":"DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenhaoxing/DeMamba","path":"util.py","file_url":"https://github.com/chenhaoxing/DeMamba/blob/HEAD/util.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":"5a62e8d84f108691","mcp_get_code":{"code_sha256":"5a62e8d84f108691"}},{"arxiv_id":"2404.10824","paper":"/paper/decoupled-weight-decay-for-any-p-norm","title":"Decoupled Weight Decay for Any $p$ Norm","date":"2024-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nadav-out/padam","path":"python/train_CIFAR10.py","file_url":"https://github.com/nadav-out/padam/blob/HEAD/python/train_CIFAR10.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6e458617fd4707aa","mcp_get_code":{"code_sha256":"6e458617fd4707aa"}},{"arxiv_id":"2404.03592","paper":"/paper/reft-representation-finetuning-for-language","title":"ReFT: Representation Finetuning for Language Models","date":"2024-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lqtrung1998/mwp_reft","path":"train_reward_model.py","file_url":"https://github.com/lqtrung1998/mwp_reft/blob/HEAD/train_reward_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":"d3b8681876990bfa","mcp_get_code":{"code_sha256":"d3b8681876990bfa"}},{"arxiv_id":"2403.20312","paper":"/paper/learn-no-to-say-yes-better-improving-vision","title":"Learn \"No\" to Say \"Yes\" Better: Improving Vision-Language Models via Negations","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaisidhsingh/con-clip","path":"src/conclip_fine_tuning.py","file_url":"https://github.com/jaisidhsingh/con-clip/blob/HEAD/src/conclip_fine_tuning.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0dbb42d74ffebd0b","mcp_get_code":{"code_sha256":"0dbb42d74ffebd0b"}},{"arxiv_id":"2403.17465","paper":"/paper/lare-2-latent-reconstruction-error-based","title":"LaRE^2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luo3300612/lare","path":"train_classifier_wmap.py","file_url":"https://github.com/luo3300612/lare/blob/HEAD/train_classifier_wmap.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":"0648e3b3ea1471b7","mcp_get_code":{"code_sha256":"0648e3b3ea1471b7"}},{"arxiv_id":"2402.12336","paper":"/paper/robust-clip-unsupervised-adversarial-fine","title":"Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chs20/robustvlm","path":"train/adversarial_training_clip.py","file_url":"https://github.com/chs20/robustvlm/blob/HEAD/train/adversarial_training_clip.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1707d13b9dc6cb32","mcp_get_code":{"code_sha256":"1707d13b9dc6cb32"}},{"arxiv_id":"2402.02491","paper":"/paper/vm-unet-vision-mamba-unet-for-medical-image","title":"VM-UNet: Vision Mamba UNet for Medical Image Segmentation","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jcruan519/vm-unet","path":"engine.py","file_url":"https://github.com/jcruan519/vm-unet/blob/HEAD/engine.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":"4709d469c5e0a024","mcp_get_code":{"code_sha256":"4709d469c5e0a024"}},{"arxiv_id":"2402.02491","paper":"/paper/vm-unet-vision-mamba-unet-for-medical-image","title":"VM-UNet: Vision Mamba UNet for Medical Image Segmentation","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jcruan519/vm-unet","path":"engine_synapse.py","file_url":"https://github.com/jcruan519/vm-unet/blob/HEAD/engine_synapse.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":"62b66c9a1c4f5a23","mcp_get_code":{"code_sha256":"62b66c9a1c4f5a23"}},{"arxiv_id":"2401.10166","paper":"/paper/vmamba-visual-state-space-model","title":"VMamba: Visual State Space Model","date":"2024-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zs1314/skinmamba","path":"engine.py","file_url":"https://github.com/zs1314/skinmamba/blob/HEAD/engine.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":"4709d469c5e0a024","mcp_get_code":{"code_sha256":"4709d469c5e0a024"}},{"arxiv_id":"2401.08967","paper":"/paper/reft-reasoning-with-reinforced-fine-tuning","title":"ReFT: Reasoning with Reinforced Fine-Tuning","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lqtrung1998/mwp_ReFT","path":"train_reward_model.py","file_url":"https://github.com/lqtrung1998/mwp_ReFT/blob/HEAD/train_reward_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":"d3b8681876990bfa","mcp_get_code":{"code_sha256":"d3b8681876990bfa"}},{"arxiv_id":"2312.15185","paper":"/paper/emotion2vec-self-supervised-pre-training-for","title":"emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation","date":"2023-12-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ddlBoJack/emotion2vec","path":"iemocap_downstream/utils.py","file_url":"https://github.com/ddlBoJack/emotion2vec/blob/HEAD/iemocap_downstream/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3f0a8af8defa1699","mcp_get_code":{"code_sha256":"3f0a8af8defa1699"}},{"arxiv_id":"2311.15939","paper":"/paper/unleashing-the-power-of-prompt-driven-nucleus","title":"Unleashing the Power of Prompt-driven Nucleus Instance Segmentation","date":"2023-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"windygoo/promptnucseg","path":"prompter/engine.py","file_url":"https://github.com/windygoo/promptnucseg/blob/HEAD/prompter/engine.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"aacd1d8b1a114829","mcp_get_code":{"code_sha256":"aacd1d8b1a114829"}},{"arxiv_id":"2311.02401","paper":"/paper/barcodebert-transformers-for-biodiversity","title":"BarcodeBERT: Transformers for Biodiversity Analysis","date":"2023-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bioscan-ml/BarcodeBERT","path":"barcodebert/finetuning.py","file_url":"https://github.com/bioscan-ml/BarcodeBERT/blob/HEAD/barcodebert/finetuning.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0af2290a64233a5e","mcp_get_code":{"code_sha256":"0af2290a64233a5e"}},{"arxiv_id":"2311.02401","paper":"/paper/barcodebert-transformers-for-biodiversity","title":"BarcodeBERT: Transformers for Biodiversity Analysis","date":"2023-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bioscan-ml/barcodebert","path":"barcodebert/pretraining.py","file_url":"https://github.com/bioscan-ml/barcodebert/blob/HEAD/barcodebert/pretraining.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"147fa47317265ee7","mcp_get_code":{"code_sha256":"147fa47317265ee7"}},{"arxiv_id":"2307.08473","paper":"/paper/ege-unet-an-efficient-group-enhanced-unet-for","title":"EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jcruan519/ege-unet","path":"engine.py","file_url":"https://github.com/jcruan519/ege-unet/blob/HEAD/engine.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":"deb813c8006d655f","mcp_get_code":{"code_sha256":"deb813c8006d655f"}},{"arxiv_id":"2303.05148","paper":"/paper/weakly-supervised-knowledge-transfer-with","title":"Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection","date":"2023-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"molden/ProbKT","path":"robust_detection/engine.py","file_url":"https://github.com/molden/ProbKT/blob/HEAD/robust_detection/engine.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c33a0bbdb30c8123","mcp_get_code":{"code_sha256":"c33a0bbdb30c8123"}},{"arxiv_id":"2301.13362","paper":"/paper/optimizing-ddpm-sampling-with-shortcut-fine","title":"Optimizing DDPM Sampling with Shortcut Fine-Tuning","date":"2023-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uw-madison-lee-lab/sft-pg","path":"finetune.py","file_url":"https://github.com/uw-madison-lee-lab/sft-pg/blob/HEAD/finetune.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a94b7f2182fe5c25","mcp_get_code":{"code_sha256":"a94b7f2182fe5c25"}},{"arxiv_id":"2208.10888","paper":"/paper/joint-privacy-enhancement-and-quantization-in","title":"Joint Privacy Enhancement and Quantization in Federated Learning","date":"2022-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"langnatalie/jopeq","path":"utils.py","file_url":"https://github.com/langnatalie/jopeq/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1e0ab9933d710b48","mcp_get_code":{"code_sha256":"1e0ab9933d710b48"}},{"arxiv_id":"2203.01137","paper":"/paper/self-supervised-scene-flow-estimation-with-4d","title":"Self-Supervised Scene Flow Estimation with 4-D Automotive Radar","date":"2022-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"toytiny/raflow","path":"main_util.py","file_url":"https://github.com/toytiny/raflow/blob/HEAD/main_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5d97447bed07b749","mcp_get_code":{"code_sha256":"5d97447bed07b749"}},{"arxiv_id":"2201.08657","paper":"/paper/enhancing-pseudo-label-quality-for-semi","title":"Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation","date":"2022-01-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xmed-lab/epl_semidg","path":"scgm_train.py","file_url":"https://github.com/xmed-lab/epl_semidg/blob/HEAD/scgm_train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bbfdf13bed603190","mcp_get_code":{"code_sha256":"bbfdf13bed603190"}},{"arxiv_id":"2112.13734","paper":"/paper/multi-domain-balanced-sampling-improves-out","title":"Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models","date":"2021-12-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"etetteh/ood_gen-chest_xray","path":"chest.py","file_url":"https://github.com/etetteh/ood_gen-chest_xray/blob/HEAD/chest.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9db258e142c77bbd","mcp_get_code":{"code_sha256":"9db258e142c77bbd"}},{"arxiv_id":"2112.12121","paper":"/paper/domain-adaptation-for-simulation-based-dark","title":"Domain Adaptation for Simulation-Based Dark Matter Searches Using Strong Gravitational Lensing","date":null,"month_inferred_from_arxiv_id":"2021-12","title_source":"archive","repo":"ml4sci/deeplense","path":"DeepLense_Gravitational_Lensing_Mriganka_Nath/Lensing_DomainAdaptation/algorithms/ADDA.py","file_url":"https://github.com/ml4sci/deeplense/blob/HEAD/DeepLense_Gravitational_Lensing_Mriganka_Nath/Lensing_DomainAdaptation/algorithms/ADDA.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5e06b026a70a1f55","mcp_get_code":{"code_sha256":"5e06b026a70a1f55"}},{"arxiv_id":"2107.02575","paper":"/paper/contrastive-multimodal-fusion-with","title":"Contrastive Multimodal Fusion with TupleInfoNCE","date":"2021-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hoi4d/TupleInfoNCE","path":"search_para.py","file_url":"https://github.com/hoi4d/TupleInfoNCE/blob/HEAD/search_para.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc73f8421e04655c","mcp_get_code":{"code_sha256":"dc73f8421e04655c"}},{"arxiv_id":"2107.02493","paper":"/paper/neighbor-vote-improving-monocular-3d-object","title":"Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting","date":"2021-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cxmomo/Neighbor-Vote","path":"tools/train_utils/train_utils.py","file_url":"https://github.com/cxmomo/Neighbor-Vote/blob/HEAD/tools/train_utils/train_utils.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":"0a2dfbac7583d7bc","mcp_get_code":{"code_sha256":"0a2dfbac7583d7bc"}},{"arxiv_id":"2104.08313","paper":"/paper/does-language-help-generalization-in-vision","title":"Does language help generalization in vision models?","date":"2021-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bdvllrs/generalization-vision","path":"tasks/transfer_learning.py","file_url":"https://github.com/bdvllrs/generalization-vision/blob/HEAD/tasks/transfer_learning.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f67d457854b1f0dd","mcp_get_code":{"code_sha256":"f67d457854b1f0dd"}},{"arxiv_id":"2104.01778","paper":"/paper/ast-audio-spectrogram-transformer","title":"AST: Audio Spectrogram Transformer","date":"2021-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pxaris/ccml","path":"training/utils.py","file_url":"https://github.com/pxaris/ccml/blob/HEAD/training/utils.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":"538dda2cb39fe247","mcp_get_code":{"code_sha256":"538dda2cb39fe247"}},{"arxiv_id":"2007.02419","paper":"/paper/attention-based-joint-detection-of-object-and","title":"Attention-based Joint Detection of Object and Semantic Part","date":"2020-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kevalmorabia97/Object-and-Semantic-Part-Detection-pyTorch","path":"references/detection/engine.py","file_url":"https://github.com/kevalmorabia97/Object-and-Semantic-Part-Detection-pyTorch/blob/HEAD/references/detection/engine.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":"5138bda1d3cc8d39","mcp_get_code":{"code_sha256":"5138bda1d3cc8d39"}},{"arxiv_id":"2001.07685","paper":"/paper/fixmatch-simplifying-semi-supervised-learning","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","date":"2020-01-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ptrckhmmr/learning-to-defer-with-limited-expert-predictions","path":"Semi-Supervised/Train_FixMatch.py","file_url":"https://github.com/ptrckhmmr/learning-to-defer-with-limited-expert-predictions/blob/HEAD/Semi-Supervised/Train_FixMatch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0db7aa7734f05609","mcp_get_code":{"code_sha256":"0db7aa7734f05609"}},{"arxiv_id":"1911.04697","paper":"/paper/phasen-a-phase-and-harmonics-aware-speech","title":"PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network","date":null,"month_inferred_from_arxiv_id":"2019-11","title_source":"archive","repo":"IMLHF/PHASEN-PyTorch","path":"phasen_torch/_2_train.py","file_url":"https://github.com/IMLHF/PHASEN-PyTorch/blob/HEAD/phasen_torch/_2_train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fbf8e30ce54ce347","mcp_get_code":{"code_sha256":"fbf8e30ce54ce347"}},{"arxiv_id":"1906.02355","paper":"/paper/neural-sde-stabilizing-neural-ode-networks","title":"Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise","date":"2019-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuanqing94/NeuralSDE","path":"train_xde.py","file_url":"https://github.com/xuanqing94/NeuralSDE/blob/HEAD/train_xde.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"527ceb9f828b115e","mcp_get_code":{"code_sha256":"527ceb9f828b115e"}},{"arxiv_id":"1703.06284","paper":"/paper/multi-talker-speech-separation-with-utterance","title":"Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks","date":"2017-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fchest/uPIT","path":"run_lstm.py","file_url":"https://github.com/fchest/uPIT/blob/HEAD/run_lstm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c5b2577f92fc75b7","mcp_get_code":{"code_sha256":"c5b2577f92fc75b7"}},{"arxiv_id":"1612.05153","paper":"/paper/on-the-potential-of-simple-framewise","title":"On the Potential of Simple Framewise Approaches to Piano Transcription","date":"2016-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rainerkelz/framewise_2016","path":"utils.py","file_url":"https://github.com/rainerkelz/framewise_2016/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d6c1b83d351192d4","mcp_get_code":{"code_sha256":"d6c1b83d351192d4"}},{"arxiv_id":"1512.02325","paper":"/paper/ssd-single-shot-multibox-detector","title":"SSD: Single Shot MultiBox Detector","date":"2015-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"krstevskipetar/SSDLite320-MobileNetV3-object-detection","path":"vision/references/detection/engine.py","file_url":"https://github.com/krstevskipetar/SSDLite320-MobileNetV3-object-detection/blob/HEAD/vision/references/detection/engine.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7194cb149a093d56","mcp_get_code":{"code_sha256":"7194cb149a093d56"}},{"arxiv_id":"ijcai2024_0675","paper":null,"title":"arXiv:ijcai2024_0675","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HongxinXiang/IEM","path":"distillation/train_utils.py","file_url":"https://github.com/HongxinXiang/IEM/blob/HEAD/distillation/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"987ebb4b147dd5d5","mcp_get_code":{"code_sha256":"987ebb4b147dd5d5"}}]}