{"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/get-parameter-groups","entry":"get_parameter_groups","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":30,"n_papers_ran":6,"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":12,"n_samples_ran":6,"n_samples_fingerprinted":0,"n_places":30,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":6,"unverified":6},"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":"2503.14237","paper":"/paper/make-your-training-flexible-towards","title":"Make Your Training Flexible: Towards Deployment-Efficient Video Models","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenGVLab/FluxViT","path":"single_modality/optim_factory.py","file_url":"https://github.com/OpenGVLab/FluxViT/blob/HEAD/single_modality/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2412.01876","paper":"/paper/understanding-bias-in-large-scale-visual","title":"Understanding Bias in Large-Scale Visual Datasets","date":"2024-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"boyazeng/understand_bias","path":"optim_factory.py","file_url":"https://github.com/boyazeng/understand_bias/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2408.06741","paper":"/paper/improving-synthetic-image-detection-towards","title":"Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective","date":"2024-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ouxiang-li/safe","path":"optim_factory.py","file_url":"https://github.com/ouxiang-li/safe/blob/HEAD/optim_factory.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":"f7189865b26b7aad","mcp_get_code":{"code_sha256":"f7189865b26b7aad"}},{"arxiv_id":"2408.03703","paper":"/paper/cas-vit-convolutional-additive-self-attention","title":"CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications","date":"2024-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianfang-zhang/cas-vit","path":"classification/optim_factory.py","file_url":"https://github.com/tianfang-zhang/cas-vit/blob/HEAD/classification/optim_factory.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"979fc7d0ba424da1","mcp_get_code":{"code_sha256":"979fc7d0ba424da1"}},{"arxiv_id":"2406.19435","paper":"/paper/a-sanity-check-for-ai-generated-image","title":"A Sanity Check for AI-generated Image Detection","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shilinyan99/aide","path":"optim_factory.py","file_url":"https://github.com/shilinyan99/aide/blob/HEAD/optim_factory.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd7ffb8631ae8a47","mcp_get_code":{"code_sha256":"dd7ffb8631ae8a47"}},{"arxiv_id":"2405.18765","paper":"/paper/large-brain-model-for-learning-generic","title":"Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"935963004/labram","path":"optim_factory.py","file_url":"https://github.com/935963004/labram/blob/HEAD/optim_factory.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"731a7e8b4363c5ab","mcp_get_code":{"code_sha256":"731a7e8b4363c5ab"}},{"arxiv_id":"2404.06773","paper":"/paper/adapting-llama-decoder-to-vision-transformer","title":"Adapting LLaMA Decoder to Vision Transformer","date":"2024-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"techmonsterwang/illama","path":"optim_factory.py","file_url":"https://github.com/techmonsterwang/illama/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2403.07560","paper":"/paper/unleashing-network-potentials-for-semantic","title":"Unleashing Network Potentials for Semantic Scene Completion","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fereenwong/ammnet","path":"ammnet/optim/optim_factory.py","file_url":"https://github.com/fereenwong/ammnet/blob/HEAD/ammnet/optim/optim_factory.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2834d68f0b087828","mcp_get_code":{"code_sha256":"2834d68f0b087828"}},{"arxiv_id":"2312.10376","paper":"/paper/sa-2-vp-spatially-aligned-and-adapted-visual","title":"SA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt","date":"2023-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tommy-xq/sa2vp","path":"optim_factory.py","file_url":"https://github.com/tommy-xq/sa2vp/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2311.05589","paper":"/paper/a-coefficient-makes-svrg-effective","title":"A Coefficient Makes SVRG Effective","date":"2023-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davidyyd/alpha-SVRG","path":"optim_factory.py","file_url":"https://github.com/davidyyd/alpha-SVRG/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2310.18884","paper":"/paper/simple-and-asymmetric-graph-contrastive-1","title":"Simple and Asymmetric Graph Contrastive Learning without Augmentations","date":"2023-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yandex-research/heterophilous-graphs","path":"utils.py","file_url":"https://github.com/yandex-research/heterophilous-graphs/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f94ccb33e6c97913","mcp_get_code":{"code_sha256":"f94ccb33e6c97913"}},{"arxiv_id":"2310.15171","paper":"/paper/robodepth-robust-out-of-distribution-depth-1","title":"RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"noahzn/Lite-Mono","path":"lite-mono-pretrain-code/optim_factory.py","file_url":"https://github.com/noahzn/Lite-Mono/blob/HEAD/lite-mono-pretrain-code/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2309.15812","paper":"/paper/convolutional-networks-with-oriented-1d-1","title":"Convolutional Networks with Oriented 1D Kernels","date":"2023-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-vl/oriented1d","path":"optim_factory.py","file_url":"https://github.com/princeton-vl/oriented1d/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2307.02227","paper":"/paper/mae-dfer-efficient-masked-autoencoder-for","title":"MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition","date":"2023-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunlicai/mae-dfer","path":"optim_factory.py","file_url":"https://github.com/sunlicai/mae-dfer/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2304.09172","paper":"/paper/hyperbolic-image-text-representations","title":"Hyperbolic Image-Text Representations","date":"2023-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/slip","path":"beit_finetuning/optim_factory.py","file_url":"https://github.com/facebookresearch/slip/blob/HEAD/beit_finetuning/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2304.02330","paper":"/paper/smpconv-self-moving-point-representations-for","title":"SMPConv: Self-moving Point Representations for Continuous Convolution","date":"2023-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sangnekim/SMPConv","path":"smp_imagenet/optim_factory.py","file_url":"https://github.com/sangnekim/SMPConv/blob/HEAD/smp_imagenet/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f752c61c80d4fa81","mcp_get_code":{"code_sha256":"f752c61c80d4fa81"}},{"arxiv_id":"2302.06052","paper":"/paper/cfnet-cascade-fusion-network-for-dense","title":"CEDNet: A Cascade Encoder-Decoder Network for Dense Prediction","date":"2023-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhanggang001/cfnet","path":"optim_factory.py","file_url":"https://github.com/zhanggang001/cfnet/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"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/optim_factory.py","file_url":"https://github.com/tum-vision/mem/blob/HEAD/mem/optim_factory.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":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2211.09120","paper":"/paper/adamae-adaptive-masking-for-efficient","title":"AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders","date":"2022-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wgcban/adamae","path":"optim_factory.py","file_url":"https://github.com/wgcban/adamae/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2211.05781","paper":"/paper/demystify-transformers-convolutions-in-modern","title":"Demystify Transformers & Convolutions in Modern Image Deep Networks","date":"2022-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/stm-evaluation","path":"classification/optim_factory.py","file_url":"https://github.com/opengvlab/stm-evaluation/blob/HEAD/classification/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"2209.03917","paper":"/paper/exploring-target-representations-for-masked","title":"Exploring Target Representations for Masked Autoencoders","date":"2022-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuxingbin/dbot","path":"evaluation/optim_factory.py","file_url":"https://github.com/liuxingbin/dbot/blob/HEAD/evaluation/optim_factory.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":"bb32ca015a494c58","mcp_get_code":{"code_sha256":"bb32ca015a494c58"}},{"arxiv_id":"2206.02967","paper":"/paper/masked-unsupervised-self-training-for-zero","title":"Masked Unsupervised Self-training for Label-free Image Classification","date":"2022-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/must","path":"optim_factory.py","file_url":"https://github.com/salesforce/must/blob/HEAD/optim_factory.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":"3698f81878031ac1","mcp_get_code":{"code_sha256":"3698f81878031ac1"}},{"arxiv_id":"2105.13677","paper":"/paper/rest-an-efficient-transformer-for-visual","title":"ResT: An Efficient Transformer for Visual Recognition","date":"2021-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wofmanaf/ResT","path":"optim_factory.py","file_url":"https://github.com/wofmanaf/ResT/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"7de76b9d1b1f4ea4","mcp_get_code":{"code_sha256":"7de76b9d1b1f4ea4"}},{"arxiv_id":"1905.05894","paper":"/paper/online-normalization-for-training-neural","title":"Online Normalization for Training Neural Networks","date":"2019-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cerebras/online-normalization","path":"experiments/resnet/utils.py","file_url":"https://github.com/cerebras/online-normalization/blob/HEAD/experiments/resnet/utils.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":"d8bd6382fa212764","mcp_get_code":{"code_sha256":"d8bd6382fa212764"}},{"arxiv_id":"1905.02244","paper":"/paper/searching-for-mobilenetv3","title":"Searching for MobileNetV3","date":"2019-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaolai-sqlai/mobilenetv3","path":"optim_factory.py","file_url":"https://github.com/xiaolai-sqlai/mobilenetv3/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"ijcai2023_0111","paper":null,"title":"arXiv:ijcai2023_0111","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"xiaolai-sqlai/RaMLP","path":"optim_factory.py","file_url":"https://github.com/xiaolai-sqlai/RaMLP/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"aaai_28243","paper":null,"title":"arXiv:aaai_28243","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"tommy-xq/SA2VP","path":"optim_factory.py","file_url":"https://github.com/tommy-xq/SA2VP/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"Li_Met2Net_A_Decoupled_Two-Stage_Spatio-Temporal_Forecasting_Model_for_Complex_Meteorological_ICCV_2025_paper","paper":null,"title":"arXiv:Li_Met2Net_A_Decoupled_Two-Stage_Spatio-Temporal_Forecasting_Model_for_Complex_Meteorological_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ShremG/Met2Net","path":"openstl/core/optim_scheduler.py","file_url":"https://github.com/ShremG/Met2Net/blob/HEAD/openstl/core/optim_scheduler.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":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"136860514","paper":null,"title":"arXiv:136860514","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"facebookresearch/SLIP","path":"beit_finetuning/optim_factory.py","file_url":"https://github.com/facebookresearch/SLIP/blob/HEAD/beit_finetuning/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}},{"arxiv_id":"136810051","paper":null,"title":"arXiv:136810051","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"raoyongming/AMixer","path":"optim_factory.py","file_url":"https://github.com/raoyongming/AMixer/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ef0019514eaaa09","mcp_get_code":{"code_sha256":"0ef0019514eaaa09"}}]}