{"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/build-scheduler","entry":"build_scheduler","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":56,"n_papers_ran":7,"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":23,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":57,"n_places_pointer_only":18,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":4,"unverified":19},"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":"2609.01136","paper":"/paper/arxiv-2609-01136","title":"Different Changes Require Different Reasoning: Change-Type-Specialized Experts for Robust Change Captioning","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"VisualAIKHU/MEDIC","path":"scheduler.py","file_url":"https://github.com/VisualAIKHU/MEDIC/blob/HEAD/scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"aedf0ba4240f8f0a","mcp_get_code":{"code_sha256":"aedf0ba4240f8f0a"}},{"arxiv_id":"2506.13277","paper":"/paper/seqpe-transformer-with-sequential-position","title":"SeqPE: Transformer with Sequential Position Encoding","date":"2025-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ghrua/seqpe","path":"image_seqpe/lr_scheduler.py","file_url":"https://github.com/ghrua/seqpe/blob/HEAD/image_seqpe/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2506.08297","paper":"/paper/sema-a-scalable-and-efficient-mamba-like","title":"SEMA: a Scalable and Efficient Mamba like Attention via Token Localization and Averaging","date":"2025-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nhatthanhtran/SEMA","path":"lr_scheduler.py","file_url":"https://github.com/nhatthanhtran/SEMA/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2412.06590","paper":"/paper/bridging-the-divide-reconsidering-softmax-and","title":"Bridging the Divide: Reconsidering Softmax and Linear Attention","date":"2024-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/inline","path":"lr_scheduler.py","file_url":"https://github.com/leaplabthu/inline/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2412.05185","paper":"/paper/linvt-empower-your-image-level-large-language","title":"LinVT: Empower Your Image-level Large Language Model to Understand Videos","date":"2024-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gls0425/linvt","path":"classification/lr_scheduler.py","file_url":"https://github.com/gls0425/linvt/blob/HEAD/classification/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8c4fa54e6e3b7fb7","mcp_get_code":{"code_sha256":"8c4fa54e6e3b7fb7"}},{"arxiv_id":"2410.15091","paper":"/paper/spatial-mamba-effective-visual-state-space","title":"Spatial-Mamba: Effective Visual State Space Models via Structure-Aware State Fusion","date":"2024-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edwardchasel/spatial-mamba","path":"classification/utils/lr_scheduler.py","file_url":"https://github.com/edwardchasel/spatial-mamba/blob/HEAD/classification/utils/lr_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":"0fbdf3257aea9bcc","mcp_get_code":{"code_sha256":"0fbdf3257aea9bcc"}},{"arxiv_id":"2407.19394","paper":"/paper/depth-wise-convolutions-in-vision","title":"Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets","date":"2024-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ztx-100/efficient_vit_with_dw","path":"lr_scheduler.py","file_url":"https://github.com/ztx-100/efficient_vit_with_dw/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0fbdf3257aea9bcc","mcp_get_code":{"code_sha256":"0fbdf3257aea9bcc"}},{"arxiv_id":"2407.10240","paper":"/paper/xlstmtime-long-term-time-series-forecasting","title":"xLSTMTime : Long-term Time Series Forecasting With xLSTM","date":"2024-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"muslehal/xLSTMTime","path":"lr_scheduler.py","file_url":"https://github.com/muslehal/xLSTMTime/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0fbdf3257aea9bcc","mcp_get_code":{"code_sha256":"0fbdf3257aea9bcc"}},{"arxiv_id":"2407.02013","paper":"/paper/digraf-diffeomorphic-graph-adaptive","title":"DiGRAF: Diffeomorphic Graph-Adaptive Activation Function","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ipsitmantri/DiTASK","path":"lr_scheduler.py","file_url":"https://github.com/ipsitmantri/DiTASK/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eca7452105889f50","mcp_get_code":{"code_sha256":"eca7452105889f50"}},{"arxiv_id":"2406.08773","paper":"/paper/denoisereid-denoising-model-for","title":"DenoiseRep: Denoising Model for Representation Learning","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangguanan/DenoiseRep","path":"Classification/imagenet/Swin-Transformer/lr_scheduler.py","file_url":"https://github.com/wangguanan/DenoiseRep/blob/HEAD/Classification/imagenet/Swin-Transformer/lr_scheduler.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":"016895042cc7e7ef","mcp_get_code":{"code_sha256":"016895042cc7e7ef"}},{"arxiv_id":"2405.16466","paper":"/paper/high-performance-temporal-reversible-spiking","title":"High-Performance Temporal Reversible Spiking Neural Networks with $O(L)$ Training Memory and $O(1)$ Inference Cost","date":"2024-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biclab/t-revsnn","path":"lr_scheduler.py","file_url":"https://github.com/biclab/t-revsnn/blob/HEAD/lr_scheduler.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c9aba121df4c6d52","mcp_get_code":{"code_sha256":"c9aba121df4c6d52"}},{"arxiv_id":"2405.11770","paper":"/paper/learning-spatial-similarity-distribution-for","title":"Learning Spatial Similarity Distribution for Few-shot Object Counting","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CBalance/SSD","path":"lr_scheduler.py","file_url":"https://github.com/CBalance/SSD/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2405.11582","paper":"/paper/slab-efficient-transformers-with-simplified","title":"SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization","date":"2024-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xinghaochen/slab","path":"classification/lr_scheduler.py","file_url":"https://github.com/xinghaochen/slab/blob/HEAD/classification/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2405.05808","paper":"/paper/fast-and-controllable-post-training-sparsity","title":"Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes","date":"2024-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ModelTC/FCPTS","path":"train/lr_scheduler.py","file_url":"https://github.com/ModelTC/FCPTS/blob/HEAD/train/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2404.07794","paper":"/paper/dgmamba-domain-generalization-via-generalized","title":"DGMamba: Domain Generalization via Generalized State Space Model","date":"2024-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"longshaocong/dgmamba","path":"utils/lr_scheduler.py","file_url":"https://github.com/longshaocong/dgmamba/blob/HEAD/utils/lr_scheduler.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"016895042cc7e7ef","mcp_get_code":{"code_sha256":"016895042cc7e7ef"}},{"arxiv_id":"2404.03015","paper":"/paper/dpft-dual-perspective-fusion-transformer-for","title":"DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection","date":"2024-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tumftm/dpft","path":"src/dprt/training/scheduler.py","file_url":"https://github.com/tumftm/dpft/blob/HEAD/src/dprt/training/scheduler.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":"a9aeaa8ba5cade44","mcp_get_code":{"code_sha256":"a9aeaa8ba5cade44"}},{"arxiv_id":"2403.20320","paper":"/paper/mtlora-a-low-rank-adaptation-approach-for","title":"MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scale-lab/mtlora","path":"lr_scheduler.py","file_url":"https://github.com/scale-lab/mtlora/blob/HEAD/lr_scheduler.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"016895042cc7e7ef","mcp_get_code":{"code_sha256":"016895042cc7e7ef"}},{"arxiv_id":"2403.02308","paper":"/paper/vision-rwkv-efficient-and-scalable-visual","title":"Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures","date":"2024-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenGVLab/Vision-RWKV","path":"classification_internimage/lr_scheduler.py","file_url":"https://github.com/OpenGVLab/Vision-RWKV/blob/HEAD/classification_internimage/lr_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":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2401.14729","paper":"/paper/sketch-and-refine-towards-fast-and-accurate","title":"Sketch and Refine: Towards Fast and Accurate Lane Detection","date":"2024-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"passerer/SRLane","path":"srlane/engine/scheduler.py","file_url":"https://github.com/passerer/SRLane/blob/HEAD/srlane/engine/scheduler.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8e2e236b88d7654e","mcp_get_code":{"code_sha256":"8e2e236b88d7654e"}},{"arxiv_id":"2401.06197","paper":"/paper/efficient-deformable-convnets-rethinking","title":"Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/dcnv4","path":"classification/lr_scheduler.py","file_url":"https://github.com/opengvlab/dcnv4/blob/HEAD/classification/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2312.08874","paper":"/paper/agent-attention-on-the-integration-of-softmax","title":"Agent Attention: On the Integration of Softmax and Linear Attention","date":"2023-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/agent-attention","path":"agent_transformer/lr_scheduler.py","file_url":"https://github.com/leaplabthu/agent-attention/blob/HEAD/agent_transformer/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2311.03912","paper":"/paper/flora-fine-grained-low-rank-architecture","title":"FLORA: Fine-grained Low-Rank Architecture Search for Vision Transformer","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shadowpa0327/flora","path":"lr_scheduler.py","file_url":"https://github.com/shadowpa0327/flora/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2311.03873","paper":"/paper/mini-but-mighty-finetuning-vits-with-mini","title":"Mini but Mighty: Finetuning ViTs with Mini Adapters","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iemprog/mimi","path":"lr_scheduler.py","file_url":"https://github.com/iemprog/mimi/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2309.01430","paper":"/paper/dat-spatially-dynamic-vision-transformer-with","title":"DAT++: Spatially Dynamic Vision Transformer with Deformable Attention","date":"2023-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/dat","path":"lr_scheduler.py","file_url":"https://github.com/leaplabthu/dat/blob/HEAD/lr_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":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2307.06947","paper":"/paper/video-focalnets-spatio-temporal-focal","title":"Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition","date":"2023-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"talalwasim/video-focalnets","path":"lr_scheduler.py","file_url":"https://github.com/talalwasim/video-focalnets/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2306.05175","paper":"/paper/large-scale-dataset-pruning-with-dynamic","title":"Large-scale Dataset Pruning with Dynamic Uncertainty","date":"2023-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baai-dcai/dataset-pruning","path":"ImageNet/lr_scheduler.py","file_url":"https://github.com/baai-dcai/dataset-pruning/blob/HEAD/ImageNet/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7474e608a870c61a","mcp_get_code":{"code_sha256":"7474e608a870c61a"}},{"arxiv_id":"2304.12043","paper":"/paper/mixpro-data-augmentation-with-maskmix-and","title":"MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer","date":"2023-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fistyee/MixPro","path":"lr_scheduler.py","file_url":"https://github.com/fistyee/MixPro/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2301.12246","paper":"/paper/a-closer-look-at-few-shot-classification","title":"A Closer Look at Few-shot Classification Again","date":"2023-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Frankluox/Pytorch-MetaDataset","path":"optimizer.py","file_url":"https://github.com/Frankluox/Pytorch-MetaDataset/blob/HEAD/optimizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"febad46f3a8e0329","mcp_get_code":{"code_sha256":"febad46f3a8e0329"}},{"arxiv_id":"2211.11694","paper":"/paper/exploring-discrete-diffusion-models-for-image","title":"Exploring Discrete Diffusion Models for Image Captioning","date":"2022-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"buxiangzhiren/ddcap","path":"lr_scheduler.py","file_url":"https://github.com/buxiangzhiren/ddcap/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5e666c43b8dba5d1","mcp_get_code":{"code_sha256":"5e666c43b8dba5d1"}},{"arxiv_id":"2208.11821","paper":"/paper/refine-and-represent-region-to-object","title":"Refine and Represent: Region-to-Object Representation Learning","date":"2022-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kkallidromitis/r2o","path":"utils/scheduler.py","file_url":"https://github.com/kkallidromitis/r2o/blob/HEAD/utils/scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a3c80de3948a6533","mcp_get_code":{"code_sha256":"a3c80de3948a6533"}},{"arxiv_id":"2207.09455","paper":"/paper/to-update-or-not-to-update-neurons-at","title":"To update or not to update? Neurons at equilibrium in deep models","date":"2022-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eidoslab/neq","path":"src/Swin-Transformer/lr_scheduler.py","file_url":"https://github.com/eidoslab/neq/blob/HEAD/src/Swin-Transformer/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2205.14141","paper":"/paper/contrastive-learning-rivals-masked-image","title":"Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation","date":"2022-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SwinTransformer/Feature-Distillation","path":"lr_scheduler.py","file_url":"https://github.com/SwinTransformer/Feature-Distillation/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"3426eb5107695fae","mcp_get_code":{"code_sha256":"3426eb5107695fae"}},{"arxiv_id":"2205.13213","paper":"/paper/fast-vision-transformers-with-hilo-attention","title":"Fast Vision Transformers with HiLo Attention","date":"2022-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuang-group/lit","path":"classification/code_for_lit_s_m_b/lr_scheduler.py","file_url":"https://github.com/zhuang-group/lit/blob/HEAD/classification/code_for_lit_s_m_b/lr_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":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2204.04916","paper":"/paper/conslt-a-token-level-contrastive-framework","title":"A Token-level Contrastive Framework for Sign Language Translation","date":"2022-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biaofuxmu/conslt","path":"signjoey/builders.py","file_url":"https://github.com/biaofuxmu/conslt/blob/HEAD/signjoey/builders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba6e0870421f0eea","mcp_get_code":{"code_sha256":"ba6e0870421f0eea"}},{"arxiv_id":"2203.10350","paper":"/paper/clrnet-cross-layer-refinement-network-for","title":"CLRNet: Cross Layer Refinement Network for Lane Detection","date":"2022-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Turoad/CLRNet","path":"clrnet/engine/scheduler.py","file_url":"https://github.com/Turoad/CLRNet/blob/HEAD/clrnet/engine/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":"ee30b884663f26dc","mcp_get_code":{"code_sha256":"ee30b884663f26dc"}},{"arxiv_id":"2203.02751","paper":"/paper/metaformer-a-unified-meta-framework-for-fine","title":"MetaFormer: A Unified Meta Framework for Fine-Grained Recognition","date":"2022-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dqshuai/metaformer","path":"lr_scheduler.py","file_url":"https://github.com/dqshuai/metaformer/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2202.06510","paper":"/paper/mixing-and-shifting-exploiting-global-and","title":"Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs","date":"2022-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jegzheng/ms-mlp","path":"lr_scheduler.py","file_url":"https://github.com/jegzheng/ms-mlp/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2109.09991","paper":"/paper/learning-kernel-smoothed-machine-translation","title":"Learning Kernel-Smoothed Machine Translation with Retrieved Examples","date":"2021-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiangqn/kster","path":"joeynmt/builders.py","file_url":"https://github.com/jiangqn/kster/blob/HEAD/joeynmt/builders.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":"ecf1add161d46a9b","mcp_get_code":{"code_sha256":"ecf1add161d46a9b"}},{"arxiv_id":"2108.02833","paper":"/paper/elaborative-rehearsal-for-zero-shot-action","title":"Elaborative Rehearsal for Zero-shot Action Recognition","date":"2021-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeLightCMU/ElaborativeRehearsal","path":"framework/lr_helper.py","file_url":"https://github.com/DeLightCMU/ElaborativeRehearsal/blob/HEAD/framework/lr_helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"75baaf5e487ddd3b","mcp_get_code":{"code_sha256":"75baaf5e487ddd3b"}},{"arxiv_id":"2107.08391","paper":"/paper/as-mlp-an-axial-shifted-mlp-architecture-for","title":"AS-MLP: An Axial Shifted MLP Architecture for Vision","date":"2021-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"svip-lab/AS-MLP","path":"lr_scheduler.py","file_url":"https://github.com/svip-lab/AS-MLP/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2106.08942","paper":"/paper/revisiting-the-weaknesses-of-reinforcement-1","title":"Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation","date":"2021-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samuki/reinforce-joey","path":"joeynmt/builders.py","file_url":"https://github.com/samuki/reinforce-joey/blob/HEAD/joeynmt/builders.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":"e0cdbcc27a1c011a","mcp_get_code":{"code_sha256":"e0cdbcc27a1c011a"}},{"arxiv_id":"2106.04263","paper":"/paper/demystifying-local-vision-transformer-sparse","title":"On the Connection between Local Attention and Dynamic Depth-wise Convolution","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Atten4Vis/DemystifyLocalViT","path":"lr_scheduler.py","file_url":"https://github.com/Atten4Vis/DemystifyLocalViT/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2106.03746","paper":"/paper/efficient-training-of-visual-transformers","title":"Efficient Training of Visual Transformers with Small Datasets","date":"2021-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhlleo/VTs-Drloc","path":"lr_scheduler.py","file_url":"https://github.com/yhlleo/VTs-Drloc/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2106.03650","paper":"/paper/shuffle-transformer-rethinking-spatial","title":"Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer","date":"2021-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mulinmeng/Shuffle-Transformer","path":"lr_scheduler.py","file_url":"https://github.com/mulinmeng/Shuffle-Transformer/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"2103.14030","paper":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"canerozer/qct","path":"lr_scheduler.py","file_url":"https://github.com/canerozer/qct/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"2003.13830","paper":"/paper/sign-language-transformers-joint-end-to-end","title":"Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neccam/slt","path":"signjoey/builders.py","file_url":"https://github.com/neccam/slt/blob/HEAD/signjoey/builders.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":"ba6e0870421f0eea","mcp_get_code":{"code_sha256":"ba6e0870421f0eea"}},{"arxiv_id":"2003.13017","paper":"/paper/fast-mvsnet-sparse-to-dense-multi-view-stereo","title":"Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement","date":"2020-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"svip-lab/FastMVSNet","path":"fastmvsnet/solver.py","file_url":"https://github.com/svip-lab/FastMVSNet/blob/HEAD/fastmvsnet/solver.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cd22297339772e3e","mcp_get_code":{"code_sha256":"cd22297339772e3e"}},{"arxiv_id":"1907.12484","paper":"/paper/joey-nmt-a-minimalist-nmt-toolkit-for-novices","title":"Joey NMT: A Minimalist NMT Toolkit for Novices","date":"2019-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jarl93/joeynmt-modified","path":"joeynmt/builders.py","file_url":"https://github.com/jarl93/joeynmt-modified/blob/HEAD/joeynmt/builders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e0cdbcc27a1c011a","mcp_get_code":{"code_sha256":"e0cdbcc27a1c011a"}},{"arxiv_id":"1907.12484","paper":"/paper/joey-nmt-a-minimalist-nmt-toolkit-for-novices","title":"Joey NMT: A Minimalist NMT Toolkit for Novices","date":"2019-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deep-spin/sigmorphon-seq2seq","path":"joeynmt/builders.py","file_url":"https://github.com/deep-spin/sigmorphon-seq2seq/blob/HEAD/joeynmt/builders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c7677be26fa62d12","mcp_get_code":{"code_sha256":"c7677be26fa62d12"}},{"arxiv_id":"ijcai2025_0271","paper":null,"title":"arXiv:ijcai2025_0271","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ZheminZhang1/HcNet","path":"HcNet/lr_scheduler.py","file_url":"https://github.com/ZheminZhang1/HcNet/blob/HEAD/HcNet/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4293bc728e424324","mcp_get_code":{"code_sha256":"4293bc728e424324"}},{"arxiv_id":"ijcai2023_0504","paper":null,"title":"arXiv:ijcai2023_0504","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Markin-Wang/CLEViT","path":"lr_scheduler.py","file_url":"https://github.com/Markin-Wang/CLEViT/blob/HEAD/lr_scheduler.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"016895042cc7e7ef","mcp_get_code":{"code_sha256":"016895042cc7e7ef"}},{"arxiv_id":"aaai_29433","paper":null,"title":"arXiv:aaai_29433","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"crystal250/GSENet","path":"clrnet/engine/scheduler.py","file_url":"https://github.com/crystal250/GSENet/blob/HEAD/clrnet/engine/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":"ee30b884663f26dc","mcp_get_code":{"code_sha256":"ee30b884663f26dc"}},{"arxiv_id":"aaai_20099","paper":null,"title":"arXiv:aaai_20099","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zip-group/LIT","path":"classification/code_for_lit_s_m_b/lr_scheduler.py","file_url":"https://github.com/zip-group/LIT/blob/HEAD/classification/code_for_lit_s_m_b/lr_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":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"Xie_PVMamba_Parallelizing_Vision_Mamba_via_Dynamic_State_Aggregation_ICCV_2025_paper","paper":null,"title":"arXiv:Xie_PVMamba_Parallelizing_Vision_Mamba_via_Dynamic_State_Aggregation_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VISION-SJTU/PVMamba","path":"classification/utils/lr_scheduler.py","file_url":"https://github.com/VISION-SJTU/PVMamba/blob/HEAD/classification/utils/lr_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":"0fbdf3257aea9bcc","mcp_get_code":{"code_sha256":"0fbdf3257aea9bcc"}},{"arxiv_id":"Ren_Masked_Jigsaw_Puzzle_A_Versatile_Position_Embedding_for_Vision_Transformers_CVPR_2023_paper","paper":null,"title":"arXiv:Ren_Masked_Jigsaw_Puzzle_A_Versatile_Position_Embedding_for_Vision_Transformers_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yhlleo/MJP","path":"lr_scheduler.py","file_url":"https://github.com/yhlleo/MJP/blob/HEAD/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c02cc4e0b5720211","mcp_get_code":{"code_sha256":"c02cc4e0b5720211"}},{"arxiv_id":"Gao_Bootstrapping_SparseFormers_from_Vision_Foundation_Models_CVPR_2024_paper","paper":null,"title":"arXiv:Gao_Bootstrapping_SparseFormers_from_Vision_Foundation_Models_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"showlab/sparseformer","path":"imagenet/lr_scheduler.py","file_url":"https://github.com/showlab/sparseformer/blob/HEAD/imagenet/lr_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}},{"arxiv_id":"00666","paper":null,"title":"arXiv:00666","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lliai/AttnZero","path":"imagenet/lr_scheduler.py","file_url":"https://github.com/lliai/AttnZero/blob/HEAD/imagenet/lr_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":"07e8b4b487c0abb6","mcp_get_code":{"code_sha256":"07e8b4b487c0abb6"}}]}