{"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-lr","entry":"get_lr","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":91,"n_papers_ran":58,"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":50,"n_samples_ran":21,"n_samples_fingerprinted":13,"n_places":94,"n_places_pointer_only":39,"by_status":{"ran_honours":14,"ran_violates":2,"ran_draft_wrong":0,"ran_fixture":0,"ran":5,"unverified":29},"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":"2606.21447","paper":"/paper/arxiv-2606-21447","title":"Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"98lingchen/MICCAI2026","path":"modules/optimizers.py","file_url":"https://github.com/98lingchen/MICCAI2026/blob/HEAD/modules/optimizers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dbc167454f546647","mcp_get_code":{"code_sha256":"dbc167454f546647"}},{"arxiv_id":"2604.01472","paper":"/paper/arxiv-2604-01472","title":"The Newton-Muon Optimizer","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"KellerJordan/modded-nanogpt","path":"records/track_1_short/2024-10-09_SOAP/train_gpt2.py","file_url":"https://github.com/KellerJordan/modded-nanogpt/blob/HEAD/records/track_1_short/2024-10-09_SOAP/train_gpt2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"16290ad6274808ff","mcp_get_code":{"code_sha256":"16290ad6274808ff"}},{"arxiv_id":"2603.14161","paper":"/paper/arxiv-2603-14161","title":"Deep probabilistic model synthesis enables unified modeling of whole-brain neural activity across individual subjects","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"neuro-will/probabilistic_model_synthesis","path":"probabilistic_model_synthesis/utilities.py","file_url":"https://github.com/neuro-will/probabilistic_model_synthesis/blob/HEAD/probabilistic_model_synthesis/utilities.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f179a4d97a2bf69b","mcp_get_code":{"code_sha256":"f179a4d97a2bf69b"}},{"arxiv_id":"2602.17080","paper":"/paper/arxiv-2602-17080","title":"Adam Improves Muon Adam Improves Muon: Adaptive Moment Estimation with Orthogonalized Momentum","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"minxin-zhg/namo","path":"src/nanogpt/train_save.py","file_url":"https://github.com/minxin-zhg/namo/blob/HEAD/src/nanogpt/train_save.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"082b9b518e204fc8","mcp_get_code":{"code_sha256":"082b9b518e204fc8"}},{"arxiv_id":"2502.20141","paper":"/paper/your-contrastive-learning-problem-is-secretly","title":"Your contrastive learning problem is secretly a distribution alignment problem","date":"2025-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"190117b425ab9fe1","mcp_get_code":{"code_sha256":"190117b425ab9fe1"}},{"arxiv_id":"2502.20141","paper":"/paper/your-contrastive-learning-problem-is-secretly","title":"Your contrastive learning problem is secretly a distribution alignment problem","date":"2025-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nerdslab/gca","path":"linear_evaluation.py","file_url":"https://github.com/nerdslab/gca/blob/HEAD/linear_evaluation.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"75b6117aca441ad6","mcp_get_code":{"code_sha256":"75b6117aca441ad6"}},{"arxiv_id":"2502.19765","paper":"/paper/editext-controllable-coarse-to-fine-text","title":"EdiText: Controllable Coarse-to-Fine Text Editing with Diffusion Language Models","date":"2025-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zacharyhorvitz/ParaGuide","path":"inference/diffusion_utils.py","file_url":"https://github.com/zacharyhorvitz/ParaGuide/blob/HEAD/inference/diffusion_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7ba6cd28cab4cdbd","mcp_get_code":{"code_sha256":"7ba6cd28cab4cdbd"}},{"arxiv_id":"2502.18153","paper":"/paper/sassha-sharpness-aware-adaptive-second-order","title":"SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation","date":"2025-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"log-postech/sassha","path":"pretraining/train_sassha.py","file_url":"https://github.com/log-postech/sassha/blob/HEAD/pretraining/train_sassha.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ef298ecd9f6a7cc0","mcp_get_code":{"code_sha256":"ef298ecd9f6a7cc0"}},{"arxiv_id":"2411.10438","paper":"/paper/mars-unleashing-the-power-of-variance","title":"MARS: Unleashing the Power of Variance Reduction for Training Large Models","date":"2024-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AGI-Arena/MARS","path":"MARS/train_mars.py","file_url":"https://github.com/AGI-Arena/MARS/blob/HEAD/MARS/train_mars.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d9145ea1a06f6d52","mcp_get_code":{"code_sha256":"d9145ea1a06f6d52"}},{"arxiv_id":"2411.06508","paper":"/paper/understanding-the-role-of-equivariance-in","title":"Understanding the Role of Equivariance in Self-supervised Learning","date":"2024-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kaotty/Understanding-ESSL","path":"ESSL/equivariant_tasks.py","file_url":"https://github.com/kaotty/Understanding-ESSL/blob/HEAD/ESSL/equivariant_tasks.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"190117b425ab9fe1","mcp_get_code":{"code_sha256":"190117b425ab9fe1"}},{"arxiv_id":"2410.14672","paper":"/paper/bigr-harnessing-binary-latent-codes-for-image","title":"BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoosz/BiGR","path":"train_ddp.py","file_url":"https://github.com/haoosz/BiGR/blob/HEAD/train_ddp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8909297ddc746f30","mcp_get_code":{"code_sha256":"8909297ddc746f30"}},{"arxiv_id":"2410.11516","paper":"/paper/topolm-brain-like-spatio-functional","title":"TopoLM: brain-like spatio-functional organization in a topographic language model","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"epflneuroailab/topolm","path":"models/finetune_glue.py","file_url":"https://github.com/epflneuroailab/topolm/blob/HEAD/models/finetune_glue.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdf4c696b10f184e","mcp_get_code":{"code_sha256":"bdf4c696b10f184e"}},{"arxiv_id":"2407.08216","paper":"/paper/multimodal-contrastive-learning-for-spatial","title":"Multimodal contrastive learning for spatial gene expression prediction using histology images","date":"2024-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shizhiceng/mclstexp","path":"utils.py","file_url":"https://github.com/shizhiceng/mclstexp/blob/HEAD/utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2407.04856","paper":"/paper/explorative-imitation-learning-a-path","title":"Explorative Imitation Learning: A Path Signature Approach for Continuous Environments","date":"2024-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2406.16976","paper":"/paper/efficient-evolutionary-search-over-chemical","title":"Efficient Evolutionary Search Over Chemical Space with Large Language Models","date":"2024-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoom-wang112358/MOLLEO","path":"multi_objective/main/molleo_multi/mol_lm.py","file_url":"https://github.com/zoom-wang112358/MOLLEO/blob/HEAD/multi_objective/main/molleo_multi/mol_lm.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea6c82ed6a138df9","mcp_get_code":{"code_sha256":"ea6c82ed6a138df9"}},{"arxiv_id":"2406.01130","paper":null,"title":"arXiv:2406.01130","date":null,"month_inferred_from_arxiv_id":"2024-06","title_source":null,"repo":"skezle/sava","path":"models/resnet.py","file_url":"https://github.com/skezle/sava/blob/HEAD/models/resnet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2405.20763","paper":"/paper/improving-generalization-and-convergence-by","title":"Improving Generalization and Convergence by Enhancing Implicit Regularization","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wmz9/ire-algorithm-framework","path":"NLP/LLM/train_admire_wiki103_Llama.py","file_url":"https://github.com/wmz9/ire-algorithm-framework/blob/HEAD/NLP/LLM/train_admire_wiki103_Llama.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5ae6fc518f0d2f24","mcp_get_code":{"code_sha256":"5ae6fc518f0d2f24"}},{"arxiv_id":"2404.16212","paper":"/paper/an-analysis-of-recent-advances-in-deepfake","title":"An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape","date":"2024-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"secml-lab-vt/EvolvingThreat-DeepfakeImageDetect","path":"adversarialattack/stylegan2-pytorch/adversarialattack_clipresnet.py","file_url":"https://github.com/secml-lab-vt/EvolvingThreat-DeepfakeImageDetect/blob/HEAD/adversarialattack/stylegan2-pytorch/adversarialattack_clipresnet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ea6c82ed6a138df9","mcp_get_code":{"code_sha256":"ea6c82ed6a138df9"}},{"arxiv_id":"2404.08634","paper":"/paper/pre-training-small-base-lms-with-fewer-tokens","title":"Inheritune: Training Smaller Yet More Attentive Language Models","date":"2024-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sanyalsunny111/llm-inheritune","path":"GPT2-experiments/train_iniheritune.py","file_url":"https://github.com/sanyalsunny111/llm-inheritune/blob/HEAD/GPT2-experiments/train_iniheritune.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8e603e20b649d63d","mcp_get_code":{"code_sha256":"8e603e20b649d63d"}},{"arxiv_id":"2403.14111","paper":"/paper/hetal-efficient-privacy-preserving-transfer","title":"HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cryptolabinc/hetal","path":"src/hetal/hyperparams.py","file_url":"https://github.com/cryptolabinc/hetal/blob/HEAD/src/hetal/hyperparams.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"537e0e24280ac134","mcp_get_code":{"code_sha256":"537e0e24280ac134"}},{"arxiv_id":"2403.10717","paper":"/paper/backdoor-secrets-unveiled-identifying","title":"Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"optml-group/backdoormspc","path":"trainnew.py","file_url":"https://github.com/optml-group/backdoormspc/blob/HEAD/trainnew.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"17b9cca691520338","mcp_get_code":{"code_sha256":"17b9cca691520338"}},{"arxiv_id":"2403.06963","paper":"/paper/the-pitfalls-of-next-token-prediction","title":"The pitfalls of next-token prediction","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gregorbachmann/next-token-failures","path":"utils/training_utils.py","file_url":"https://github.com/gregorbachmann/next-token-failures/blob/HEAD/utils/training_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1b9a2fc53c54c9be","mcp_get_code":{"code_sha256":"1b9a2fc53c54c9be"}},{"arxiv_id":"2402.18153","paper":"/paper/diffusion-based-neural-network-weights","title":"Diffusion-Based Neural Network Weights Generation","date":"2024-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2402.16788","paper":"/paper/why-transformers-need-adam-a-hessian","title":"Why Transformers Need Adam: A Hessian Perspective","date":"2024-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyushun/hessian-spectrum","path":"language_models/train_gpt2.py","file_url":"https://github.com/zyushun/hessian-spectrum/blob/HEAD/language_models/train_gpt2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"aaad22245780ab53","mcp_get_code":{"code_sha256":"aaad22245780ab53"}},{"arxiv_id":"2402.06244","paper":"/paper/quantifying-and-enhancing-multi-modal","title":"Quantifying and Enhancing Multi-modal Robustness with Modality Preference","date":"2024-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GeWu-Lab/Certifiable-Robust-Multi-modal-Training","path":"utils.py","file_url":"https://github.com/GeWu-Lab/Certifiable-Robust-Multi-modal-Training/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a11eecab8e7e04af","mcp_get_code":{"code_sha256":"a11eecab8e7e04af"}},{"arxiv_id":"2401.06155","paper":"/paper/de-novo-drug-design-using-reinforcement-1","title":"De novo Drug Design using Reinforcement Learning with Multiple GPT Agents","date":"2023-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HXYfighter/MolRL-MGPT","path":"codes/pretrain.py","file_url":"https://github.com/HXYfighter/MolRL-MGPT/blob/HEAD/codes/pretrain.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"96801921120b1228","mcp_get_code":{"code_sha256":"96801921120b1228"}},{"arxiv_id":"2312.05767","paper":"/paper/anomalydiffusion-few-shot-anomaly-image","title":"AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model","date":"2023-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtuplayer/anomalydiffusion","path":"test-classification.py","file_url":"https://github.com/sjtuplayer/anomalydiffusion/blob/HEAD/test-classification.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2312.02517","paper":"/paper/simplifying-neural-network-training-under-1","title":"Simplifying Neural Network Training Under Class Imbalance","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"190117b425ab9fe1","mcp_get_code":{"code_sha256":"190117b425ab9fe1"}},{"arxiv_id":"2310.04780","paper":"/paper/ipmix-label-preserving-data-augmentation-1","title":"IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers","date":"2023-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"190117b425ab9fe1","mcp_get_code":{"code_sha256":"190117b425ab9fe1"}},{"arxiv_id":"2309.10661","paper":"/paper/nusawrites-constructing-high-quality-corpora","title":"NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages","date":"2023-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"indonlp/nusa-writes","path":"main_generation.py","file_url":"https://github.com/indonlp/nusa-writes/blob/HEAD/main_generation.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e9c7d263fd12f86d","mcp_get_code":{"code_sha256":"e9c7d263fd12f86d"}},{"arxiv_id":"2307.06865","paper":"/paper/prompts-should-not-be-seen-as-secrets","title":"Effective Prompt Extraction from Language Models","date":"2023-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"y0mingzhang/prompt-extraction","path":"src/finetune-similarity-predictor.py","file_url":"https://github.com/y0mingzhang/prompt-extraction/blob/HEAD/src/finetune-similarity-predictor.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c6f7efd724869cc1","mcp_get_code":{"code_sha256":"c6f7efd724869cc1"}},{"arxiv_id":"2306.12755","paper":"/paper/beyond-ood-state-actions-supported-cross","title":"Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning","date":"2023-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuml/SPOT","path":"utils.py","file_url":"https://github.com/thuml/SPOT/blob/HEAD/utils.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2306.03715","paper":"/paper/unleashing-mask-explore-the-intrinsic-out-of","title":"Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability","date":"2023-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlr-group/unleashing-mask","path":"utils/net_utils.py","file_url":"https://github.com/tmlr-group/unleashing-mask/blob/HEAD/utils/net_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"717f5458343a0b63","mcp_get_code":{"code_sha256":"717f5458343a0b63"}},{"arxiv_id":"2306.01859","paper":"/paper/spatially-resolved-gene-expression-prediction","title":"Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning","date":"2023-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bowang-lab/bleep","path":"utils.py","file_url":"https://github.com/bowang-lab/bleep/blob/HEAD/utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2305.15316","paper":"/paper/training-on-thin-air-improve-image","title":"Training on Thin Air: Improve Image Classification with Generated Data","date":"2023-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yongchao97/diffusion_inversion","path":"src/train_net.py","file_url":"https://github.com/yongchao97/diffusion_inversion/blob/HEAD/src/train_net.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2305.14342","paper":"/paper/sophia-a-scalable-stochastic-second-order","title":"Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Liuhong99/Sophia","path":"train_sophiag.py","file_url":"https://github.com/Liuhong99/Sophia/blob/HEAD/train_sophiag.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a273291a5ece194d","mcp_get_code":{"code_sha256":"a273291a5ece194d"}},{"arxiv_id":"2210.08196","paper":"/paper/deep-regression-unlearning","title":"Deep Regression Unlearning","date":"2022-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2205.14620","paper":"/paper/ifrnet-intermediate-feature-refine-network","title":"IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation","date":"2022-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltkong218/IFRNet","path":"train_gopro.py","file_url":"https://github.com/ltkong218/IFRNet/blob/HEAD/train_gopro.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aaa35a6320121aa6","mcp_get_code":{"code_sha256":"aaa35a6320121aa6"}},{"arxiv_id":"2203.17266","paper":"/paper/transeditor-transformer-based-dual-space-gan","title":"TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing","date":"2022-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"billyxyb/transeditor","path":"projector_optimization.py","file_url":"https://github.com/billyxyb/transeditor/blob/HEAD/projector_optimization.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea6c82ed6a138df9","mcp_get_code":{"code_sha256":"ea6c82ed6a138df9"}},{"arxiv_id":"2202.06258","paper":"/paper/flowformer-linearizing-transformers-with","title":"Flowformer: Linearizing Transformers with Conservation Flows","date":"2022-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuml/Flowformer","path":"Flowformer_RL/utils.py","file_url":"https://github.com/thuml/Flowformer/blob/HEAD/Flowformer_RL/utils.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2202.01341","paper":"/paper/robust-binary-models-by-pruning-randomly","title":"Robust Binary Models by Pruning Randomly-initialized Networks","date":"2022-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IVRL/RobustBinarySubNet","path":"code/arch/net_utils.py","file_url":"https://github.com/IVRL/RobustBinarySubNet/blob/HEAD/code/arch/net_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"717f5458343a0b63","mcp_get_code":{"code_sha256":"717f5458343a0b63"}},{"arxiv_id":"2112.11450","paper":"/paper/max-margin-contrastive-learning","title":"Max-Margin Contrastive Learning","date":"2021-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anshulbshah/MMCL","path":"CIFAR100/linear.py","file_url":"https://github.com/anshulbshah/MMCL/blob/HEAD/CIFAR100/linear.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2112.05135","paper":"/paper/pixmix-dreamlike-pictures-comprehensively","title":"PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"190117b425ab9fe1","mcp_get_code":{"code_sha256":"190117b425ab9fe1"}},{"arxiv_id":"2112.05135","paper":"/paper/pixmix-dreamlike-pictures-comprehensively","title":"PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joe1chief/windownormalizaion","path":"cifar.py","file_url":"https://github.com/joe1chief/windownormalizaion/blob/HEAD/cifar.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"479f80cec85ca5af","mcp_get_code":{"code_sha256":"479f80cec85ca5af"}},{"arxiv_id":"2112.01161","paper":"/paper/video-frame-interpolation-without-temporal-1","title":"Video Frame Interpolation without Temporal Priors","date":"2021-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yjzhang96/UTI-VFI","path":"train_deblur.py","file_url":"https://github.com/yjzhang96/UTI-VFI/blob/HEAD/train_deblur.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2111.12330","paper":"/paper/hidden-fold-networks-random-recurrent","title":"Hidden-Fold Networks: Random Recurrent Residuals Using Sparse Supermasks","date":"2021-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lopez-Angel/hidden-fold-networks","path":"utils/net_utils.py","file_url":"https://github.com/Lopez-Angel/hidden-fold-networks/blob/HEAD/utils/net_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":"717f5458343a0b63","mcp_get_code":{"code_sha256":"717f5458343a0b63"}},{"arxiv_id":"2111.12273","paper":"/paper/sharpness-aware-quantization-for-deep-neural","title":"Sharpness-aware Quantization for Deep Neural Networks","date":"2021-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuang-group/saq","path":"core/engine.py","file_url":"https://github.com/zhuang-group/saq/blob/HEAD/core/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":"601ccb52d01d6f2c","mcp_get_code":{"code_sha256":"601ccb52d01d6f2c"}},{"arxiv_id":"2110.14068","paper":"/paper/drawing-robust-scratch-tickets-subnetworks","title":"Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RICE-EIC/Robust-Scratch-Ticket","path":"utils/net_utils.py","file_url":"https://github.com/RICE-EIC/Robust-Scratch-Ticket/blob/HEAD/utils/net_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"717f5458343a0b63","mcp_get_code":{"code_sha256":"717f5458343a0b63"}},{"arxiv_id":"2110.12427","paper":"/paper/image-based-clip-guided-essence-transfer","title":"Image-Based CLIP-Guided Essence Transfer","date":"2021-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hila-chefer/targetclip","path":"optimization.py","file_url":"https://github.com/hila-chefer/targetclip/blob/HEAD/optimization.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9ef8957c513ffb18","mcp_get_code":{"code_sha256":"9ef8957c513ffb18"}},{"arxiv_id":"2110.11945","paper":"/paper/soft-softmax-free-transformer-with-linear","title":"SOFT: Softmax-free Transformer with Linear Complexity","date":"2021-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fudan-zvg/SOFT_MindSpore_Ascend","path":"src/lr_generator.py","file_url":"https://github.com/fudan-zvg/SOFT_MindSpore_Ascend/blob/HEAD/src/lr_generator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d2729f1965abbac9","mcp_get_code":{"code_sha256":"d2729f1965abbac9"}},{"arxiv_id":"2109.10686","paper":"/paper/scale-efficiently-insights-from-pre-training","title":"Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers","date":"2021-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gsarti/it5","path":"finetuning/vars.py","file_url":"https://github.com/gsarti/it5/blob/HEAD/finetuning/vars.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":"c4f59346ce8a05d9","mcp_get_code":{"code_sha256":"c4f59346ce8a05d9"}},{"arxiv_id":"2109.09850","paper":"/paper/balanced-mixup-for-highly-imbalanced-medical","title":"Balanced-MixUp for Highly Imbalanced Medical Image Classification","date":"2021-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2109.07684","paper":"/paper/language-models-are-few-shot-multilingual","title":"Language Models are Few-shot Multilingual Learners","date":"2021-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gentaiscool/few-shot-lm","path":"evaluate.py","file_url":"https://github.com/gentaiscool/few-shot-lm/blob/HEAD/evaluate.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e9c7d263fd12f86d","mcp_get_code":{"code_sha256":"e9c7d263fd12f86d"}},{"arxiv_id":"2108.07610","paper":"/paper/draem-a-discriminatively-trained","title":"DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection","date":"2021-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vitjanz/draem","path":"train_DRAEM.py","file_url":"https://github.com/vitjanz/draem/blob/HEAD/train_DRAEM.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2106.12423","paper":"/paper/alias-free-generative-adversarial-networks","title":"Alias-Free Generative Adversarial Networks","date":"2021-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"ea6c82ed6a138df9","mcp_get_code":{"code_sha256":"ea6c82ed6a138df9"}},{"arxiv_id":"2106.08181","paper":"/paper/direction-is-what-you-need-improving-word","title":"Direction is what you need: Improving Word Embedding Compression in Large Language Models","date":"2021-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MohammadrezaBanaei/orientation_based_embedding_compression","path":"ae_train.py","file_url":"https://github.com/MohammadrezaBanaei/orientation_based_embedding_compression/blob/HEAD/ae_train.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2105.14230","paper":"/paper/transforming-the-latent-space-of-stylegan-for","title":"Transforming the Latent Space of StyleGAN for Real Face Editing","date":"2021-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AnonSubm2021/TransStyleGAN","path":"projector.py","file_url":"https://github.com/AnonSubm2021/TransStyleGAN/blob/HEAD/projector.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ea6c82ed6a138df9","mcp_get_code":{"code_sha256":"ea6c82ed6a138df9"}},{"arxiv_id":"2102.06108","paper":"/paper/swagan-a-style-based-wavelet-driven","title":"SWAGAN: A Style-based Wavelet-driven Generative Model","date":"2021-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"ea6c82ed6a138df9","mcp_get_code":{"code_sha256":"ea6c82ed6a138df9"}},{"arxiv_id":"2101.06006","paper":"/paper/the-geometry-of-deep-generative-image-models","title":"The Geometry of Deep Generative Image Models and its Applications","date":"2021-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"ea6c82ed6a138df9","mcp_get_code":{"code_sha256":"ea6c82ed6a138df9"}},{"arxiv_id":"2012.11581","paper":"/paper/populating-3d-scenes-by-learning-human-scene","title":"Populating 3D Scenes by Learning Human-Scene Interaction","date":"2020-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2009.05387","paper":"/paper/indonlu-benchmark-and-resources-for","title":"IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding","date":"2020-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"indobenchmark/indonlu","path":"predict.py","file_url":"https://github.com/indobenchmark/indonlu/blob/HEAD/predict.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e9c7d263fd12f86d","mcp_get_code":{"code_sha256":"e9c7d263fd12f86d"}},{"arxiv_id":"2007.04417","paper":"/paper/smaat-unet-precipitation-nowcasting-using-a","title":"SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture","date":"2020-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HansBambel/SmaAt-UNet","path":"train_SmaAtUNet.py","file_url":"https://github.com/HansBambel/SmaAt-UNet/blob/HEAD/train_SmaAtUNet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2006.07682","paper":"/paper/clusttr-clustering-training-for-robustness","title":"Rethinking Clustering for Robustness","date":"2020-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clustr-official-account/ClusTR-Clustering-Training-For-Robustness","path":"utils/logging.py","file_url":"https://github.com/clustr-official-account/ClusTR-Clustering-Training-For-Robustness/blob/HEAD/utils/logging.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2005.14165","paper":"/paper/language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ramanakshay/nanogpt","path":"src/algorithm/optimizer.py","file_url":"https://github.com/ramanakshay/nanogpt/blob/HEAD/src/algorithm/optimizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b1e0531f8c17d5d6","mcp_get_code":{"code_sha256":"b1e0531f8c17d5d6"}},{"arxiv_id":"2005.03788","paper":"/paper/proselflc-progressive-self-label-correction","title":"ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks","date":"2020-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kiyoon/pyvideoai","path":"pyvideoai/train_and_eval.py","file_url":"https://github.com/kiyoon/pyvideoai/blob/HEAD/pyvideoai/train_and_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"532347378db99392","mcp_get_code":{"code_sha256":"532347378db99392"}},{"arxiv_id":"2003.13678","paper":"/paper/designing-network-design-spaces","title":"Designing Network Design Spaces","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wilile26811249/RegNet","path":"utils.py","file_url":"https://github.com/wilile26811249/RegNet/blob/HEAD/utils.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2002.10025","paper":"/paper/triple-wins-boosting-accuracy-robustness-and-1","title":"Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference","date":"2020-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gorakraj/earlyexit_onnx","path":"Networks/8. L2Stop/l2stop-master/sdn_stop/architectures/SDNs/ResNet_SDN.py","file_url":"https://github.com/gorakraj/earlyexit_onnx/blob/HEAD/Networks/8.%20L2Stop/l2stop-master/sdn_stop/architectures/SDNs/ResNet_SDN.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1aca7be4e59a01a0","mcp_get_code":{"code_sha256":"1aca7be4e59a01a0"}},{"arxiv_id":"2002.05709","paper":"/paper/a-simple-framework-for-contrastive-learning","title":"A Simple Framework for Contrastive Learning of Visual Representations","date":"2020-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"p3i0t/SimCLR-CIFAR10","path":"simclr.py","file_url":"https://github.com/p3i0t/SimCLR-CIFAR10/blob/HEAD/simclr.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"190117b425ab9fe1","mcp_get_code":{"code_sha256":"190117b425ab9fe1"}},{"arxiv_id":"1912.04958","paper":"/paper/analyzing-and-improving-the-image-quality-of","title":"Analyzing and Improving the Image Quality of StyleGAN","date":"2019-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mbbrodie/stylegan2","path":"projector.py","file_url":"https://github.com/mbbrodie/stylegan2/blob/HEAD/projector.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea6c82ed6a138df9","mcp_get_code":{"code_sha256":"ea6c82ed6a138df9"}},{"arxiv_id":"1912.02923","paper":"/paper/generating-3d-people-in-scenes-without-people","title":"Generating 3D People in Scenes without People","date":"2019-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"1911.13299","paper":"/paper/whats-hidden-in-a-randomly-weighted-neural","title":"What's Hidden in a Randomly Weighted Neural Network?","date":"2019-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/hidden-networks","path":"utils/net_utils.py","file_url":"https://github.com/allenai/hidden-networks/blob/HEAD/utils/net_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":"717f5458343a0b63","mcp_get_code":{"code_sha256":"717f5458343a0b63"}},{"arxiv_id":"1911.11907","paper":"/paper/ghostnet-more-features-from-cheap-operations","title":"GhostNet: More Features from Cheap Operations","date":"2019-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0jason000/S-GhostNet","path":"src/utils.py","file_url":"https://github.com/0jason000/S-GhostNet/blob/HEAD/src/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":"9dfb4495e7fb2efb","mcp_get_code":{"code_sha256":"9dfb4495e7fb2efb"}},{"arxiv_id":"1911.11907","paper":"/paper/ghostnet-more-features-from-cheap-operations","title":"GhostNet: More Features from Cheap Operations","date":"2019-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangyucheng000/ghostnet","path":"src/lr_generator.py","file_url":"https://github.com/yangyucheng000/ghostnet/blob/HEAD/src/lr_generator.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":"78ea6999b2c97419","mcp_get_code":{"code_sha256":"78ea6999b2c97419"}},{"arxiv_id":"1910.00643","paper":"/paper/slowmo-improving-communication-efficient","title":"SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum","date":"2019-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shuhuayu/dist-sign-momentum","path":"nanoGPT/train_ddp.py","file_url":"https://github.com/shuhuayu/dist-sign-momentum/blob/HEAD/nanoGPT/train_ddp.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":"bdf4c696b10f184e","mcp_get_code":{"code_sha256":"bdf4c696b10f184e"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JoegameZhou/efficientnet-b0","path":"src/lr_generator.py","file_url":"https://github.com/JoegameZhou/efficientnet-b0/blob/HEAD/src/lr_generator.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":"ccb82a7bb4dc4e76","mcp_get_code":{"code_sha256":"ccb82a7bb4dc4e76"}},{"arxiv_id":"1905.07785","paper":"/paper/sparse-transfer-learning-via-winning-lottery","title":"Sparse Transfer Learning via Winning Lottery Tickets","date":"2019-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rahulsmehta/sparsity-experiments","path":"experiments/lt-iterative-cifar-resnet18.py","file_url":"https://github.com/rahulsmehta/sparsity-experiments/blob/HEAD/experiments/lt-iterative-cifar-resnet18.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2254ab9d7a4913ee","mcp_get_code":{"code_sha256":"2254ab9d7a4913ee"}},{"arxiv_id":"1905.03670","paper":"/paper/190503670","title":"S4L: Self-Supervised Semi-Supervised Learning","date":"2019-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/s4l","path":"trainer.py","file_url":"https://github.com/google-research/s4l/blob/HEAD/trainer.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":"314010398689c491","mcp_get_code":{"code_sha256":"314010398689c491"}},{"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":"atregret/mobilenetv3","path":"src/lr_generator.py","file_url":"https://github.com/atregret/mobilenetv3/blob/HEAD/src/lr_generator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bcab367eb4c6388e","mcp_get_code":{"code_sha256":"bcab367eb4c6388e"}},{"arxiv_id":"1904.01355","paper":"/paper/fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"1901.09005","paper":"/paper/revisiting-self-supervised-visual","title":"Revisiting Self-Supervised Visual Representation Learning","date":"2019-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rickyHong/Puzzle-tensorflow-latest-repl","path":"trainer.py","file_url":"https://github.com/rickyHong/Puzzle-tensorflow-latest-repl/blob/HEAD/trainer.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":"baf26176db8e0350","mcp_get_code":{"code_sha256":"baf26176db8e0350"}},{"arxiv_id":"1901.06384","paper":"/paper/supernnova-an-open-source-framework-for","title":"SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification","date":null,"month_inferred_from_arxiv_id":"2019-01","title_source":"archive","repo":"supernnova/SuperNNova","path":"python/supernnova/training/train_rnn.py","file_url":"https://github.com/supernnova/SuperNNova/blob/HEAD/python/supernnova/training/train_rnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"377ca96d9a3cdd26","mcp_get_code":{"code_sha256":"377ca96d9a3cdd26"}},{"arxiv_id":"1811.12814","paper":"/paper/graph-based-global-reasoning-networks","title":"Graph-Based Global Reasoning Networks","date":"2018-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangyucheng000/glore_res200","path":"src/lr_generator.py","file_url":"https://github.com/yangyucheng000/glore_res200/blob/HEAD/src/lr_generator.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":"ccb82a7bb4dc4e76","mcp_get_code":{"code_sha256":"ccb82a7bb4dc4e76"}},{"arxiv_id":"1807.06521","paper":"/paper/cbam-convolutional-block-attention-module","title":"CBAM: Convolutional Block Attention Module","date":"2018-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LKLQQ/CBAM","path":"src/get_lr.py","file_url":"https://github.com/LKLQQ/CBAM/blob/HEAD/src/get_lr.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":"88b73ea143b80bda","mcp_get_code":{"code_sha256":"88b73ea143b80bda"}},{"arxiv_id":"1806.03185","paper":"/paper/wave-u-net-a-multi-scale-neural-network-for","title":"Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation","date":"2018-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"f90/Wave-U-Net-Pytorch","path":"utils.py","file_url":"https://github.com/f90/Wave-U-Net-Pytorch/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":"b293995d32808cdd","mcp_get_code":{"code_sha256":"b293995d32808cdd"}},{"arxiv_id":"1711.08324","paper":"/paper/evaluate-the-malignancy-of-pulmonary-nodules","title":"Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network","date":"2017-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lfz/DSB2017","path":"training/classifier/trainval_classifier.py","file_url":"https://github.com/lfz/DSB2017/blob/HEAD/training/classifier/trainval_classifier.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":"2e05b376cfc8bf47","mcp_get_code":{"code_sha256":"2e05b376cfc8bf47"}},{"arxiv_id":"1611.06612","paper":"/paper/refinenet-multi-path-refinement-networks-for","title":"RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation","date":"2016-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alililia/ascend_RefineNet","path":"src/learning_rates.py","file_url":"https://github.com/alililia/ascend_RefineNet/blob/HEAD/src/learning_rates.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":"ca5006a85f504b0d","mcp_get_code":{"code_sha256":"ca5006a85f504b0d"}},{"arxiv_id":"1606.00915","paper":"/paper/deeplab-semantic-image-segmentation-with-deep","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","date":"2016-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eceo-epfl/scaleprotoseg","path":"segmentation/em/module_em.py","file_url":"https://github.com/eceo-epfl/scaleprotoseg/blob/HEAD/segmentation/em/module_em.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"1605.07148","paper":"/paper/backprop-kf-learning-discriminative","title":"Backprop KF: Learning Discriminative Deterministic State Estimators","date":"2016-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"1605.07146","paper":"/paper/wide-residual-networks","title":"Wide Residual Networks","date":"2016-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alililia/wideresnet_Ascend","path":"src/generator_lr.py","file_url":"https://github.com/alililia/wideresnet_Ascend/blob/HEAD/src/generator_lr.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":"944d7754b35182e3","mcp_get_code":{"code_sha256":"944d7754b35182e3"}},{"arxiv_id":"1512.02595","paper":"/paper/deep-speech-2-end-to-end-speech-recognition","title":"Deep Speech 2: End-to-End Speech Recognition in English and Mandarin","date":"2015-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"freshtan/deepspeech2","path":"src/lr_generator.py","file_url":"https://github.com/freshtan/deepspeech2/blob/HEAD/src/lr_generator.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":"2adf2c08a0fbed04","mcp_get_code":{"code_sha256":"2adf2c08a0fbed04"}},{"arxiv_id":"1512.00567","paper":"/paper/rethinking-the-inception-architecture-for","title":"Rethinking the Inception Architecture for Computer Vision","date":"2015-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LKLQQ/Inception-v2","path":"src/lr_generator.py","file_url":"https://github.com/LKLQQ/Inception-v2/blob/HEAD/src/lr_generator.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":"36cd7310bc74d09d","mcp_get_code":{"code_sha256":"36cd7310bc74d09d"}},{"arxiv_id":"1502.03167","paper":"/paper/batch-normalization-accelerating-deep-network","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","date":"2015-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangyucheng000/ssd_inception_v2","path":"src/lr_schedule.py","file_url":"https://github.com/yangyucheng000/ssd_inception_v2/blob/HEAD/src/lr_schedule.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":"1bab04258df1af4e","mcp_get_code":{"code_sha256":"1bab04258df1af4e"}},{"arxiv_id":"Zavrtanik_DRAEM_-_A_Discriminatively_Trained_Reconstruction_Embedding_for_Surface_Anomaly_ICCV_2021_paper","paper":null,"title":"arXiv:Zavrtanik_DRAEM_-_A_Discriminatively_Trained_Reconstruction_Embedding_for_Surface_Anomaly_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VitjanZ/DRAEM","path":"train_DRAEM.py","file_url":"https://github.com/VitjanZ/DRAEM/blob/HEAD/train_DRAEM.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":"7337f1f5ff01dcd0","mcp_get_code":{"code_sha256":"7337f1f5ff01dcd0"}},{"arxiv_id":"2021.acl-long.459","paper":null,"title":"arXiv:2021.acl-long.459","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cuhksz-nlp/R2GenCMN","path":"modules/optimizers.py","file_url":"https://github.com/cuhksz-nlp/R2GenCMN/blob/HEAD/modules/optimizers.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":"dbc167454f546647","mcp_get_code":{"code_sha256":"dbc167454f546647"}}]}