{"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/normalization","entry":"normalization","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":70,"n_papers_ran":48,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":52,"n_samples_ran":33,"n_samples_fingerprinted":9,"n_places":75,"n_places_pointer_only":35,"by_status":{"ran_honours":2,"ran_violates":1,"ran_draft_wrong":15,"ran_fixture":1,"ran":14,"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":"2606.18209","paper":"/paper/arxiv-2606-18209","title":"Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"vimar-gu/MinimaxDiffusion","path":"train_models/resnet.py","file_url":"https://github.com/vimar-gu/MinimaxDiffusion/blob/HEAD/train_models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8aac35516308515a","mcp_get_code":{"code_sha256":"8aac35516308515a"}},{"arxiv_id":"2606.18209","paper":"/paper/arxiv-2606-18209","title":"Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"vimar-gu/MinimaxDiffusion","path":"train_models/resnet_ap.py","file_url":"https://github.com/vimar-gu/MinimaxDiffusion/blob/HEAD/train_models/resnet_ap.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8ba16d20cf03f09b","mcp_get_code":{"code_sha256":"8ba16d20cf03f09b"}},{"arxiv_id":"2604.24201","paper":"/paper/arxiv-2604-24201","title":"CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"chenzRG/Cancer-Multi-Omics-Benchmark","path":"Baseline_and_Metric/Imputation/GAIN/utils.py","file_url":"https://github.com/chenzRG/Cancer-Multi-Omics-Benchmark/blob/HEAD/Baseline_and_Metric/Imputation/GAIN/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"82b1e0d73f4df6cd","mcp_get_code":{"code_sha256":"82b1e0d73f4df6cd"}},{"arxiv_id":"2602.01570","paper":"/paper/arxiv-2602-01570","title":"One-Step Diffusion for Perceptual Image Compression","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"cheesejiang/OSDiff","path":"model/osdiff.py","file_url":"https://github.com/cheesejiang/OSDiff/blob/HEAD/model/osdiff.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":"2cc202bbb038146d","mcp_get_code":{"code_sha256":"2cc202bbb038146d"}},{"arxiv_id":"2601.19498","paper":"/paper/arxiv-2601-19498","title":"Cortex-Grounded Diffusion Models for Brain Image Generation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"ai-med/Cor2Vox","path":"model/BrownianBridge/BrownianBridgeModel_c2v.py","file_url":"https://github.com/ai-med/Cor2Vox/blob/HEAD/model/BrownianBridge/BrownianBridgeModel_c2v.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"542f1a55e9dbd14a","mcp_get_code":{"code_sha256":"542f1a55e9dbd14a"}},{"arxiv_id":"2601.19349","paper":"/paper/arxiv-2601-19349","title":"AMGFormer: Adaptive Multi-Granular Transformer for Brain Tumor Segmentation with Missing Modalities","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"guochengxiangives/AMGFormer","path":"amgformer/layers.py","file_url":"https://github.com/guochengxiangives/AMGFormer/blob/HEAD/amgformer/layers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"376f134d99d8ed75","mcp_get_code":{"code_sha256":"376f134d99d8ed75"}},{"arxiv_id":"2601.17529","paper":"/paper/arxiv-2601-17529","title":"FMIR, A Foundation Model-Based Image Registration Framework for Robust Image Registration","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Monday0328/FMIR","path":"loaders/acdcreg_loader.py","file_url":"https://github.com/Monday0328/FMIR/blob/HEAD/loaders/acdcreg_loader.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"86c7147edb17dd5d","mcp_get_code":{"code_sha256":"86c7147edb17dd5d"}},{"arxiv_id":"2601.13502","paper":"/paper/arxiv-2601-13502","title":"DIS2: Disentanglement Meets Distillation with Classwise Attention for Robust Remote Sensing Segmentation under Missing Modalities","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"nhikieu/DIS2","path":"models_bank/DLKD_ver4.py","file_url":"https://github.com/nhikieu/DIS2/blob/HEAD/models_bank/DLKD_ver4.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d3c5e61ad2232b1d","mcp_get_code":{"code_sha256":"d3c5e61ad2232b1d"}},{"arxiv_id":"2601.04960","paper":"/paper/arxiv-2601-04960","title":"A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"ASLP-lab/Hum-Dial","path":"Full-Duplex_Interaction/baseline/f5_tts/model/arch.py","file_url":"https://github.com/ASLP-lab/Hum-Dial/blob/HEAD/Full-Duplex_Interaction/baseline/f5_tts/model/arch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5ebeb898213a20c9","mcp_get_code":{"code_sha256":"5ebeb898213a20c9"}},{"arxiv_id":"2601.00328","paper":"/paper/arxiv-2601-00328","title":"Joint Geometry-Appearance Human Reconstruction in a Unified Latent Space via Bridge Diffusion","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"haiantyz/JGA-LBD","path":"JGA-LBD/DDBM/ddbm/unet3d.py","file_url":"https://github.com/haiantyz/JGA-LBD/blob/HEAD/JGA-LBD/DDBM/ddbm/unet3d.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8337c76a7f90e95b","mcp_get_code":{"code_sha256":"8337c76a7f90e95b"}},{"arxiv_id":"2511.22549","paper":"/paper/arxiv-2511-22549","title":"Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"RuoyuFeng/Diff-ICMH","path":"model/diffeic.py","file_url":"https://github.com/RuoyuFeng/Diff-ICMH/blob/HEAD/model/diffeic.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":"2cc202bbb038146d","mcp_get_code":{"code_sha256":"2cc202bbb038146d"}},{"arxiv_id":"2504.02807","paper":"/paper/megamath-pushing-the-limits-of-open-math","title":"MegaMath: Pushing the Limits of Open Math Corpora","date":"2025-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"llm360/megamath","path":"web_pipeline/utils/math_fasttext.py","file_url":"https://github.com/llm360/megamath/blob/HEAD/web_pipeline/utils/math_fasttext.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":"94cea2fe405ab4af","mcp_get_code":{"code_sha256":"94cea2fe405ab4af"}},{"arxiv_id":"2503.09679","paper":"/paper/dress-disentangled-representation-based-self","title":"DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks","date":"2025-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"layer6ai-labs/DRESS","path":"encoders/diti.py","file_url":"https://github.com/layer6ai-labs/DRESS/blob/HEAD/encoders/diti.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"992e95480b7cbca2","mcp_get_code":{"code_sha256":"992e95480b7cbca2"}},{"arxiv_id":"2502.09873","paper":"/paper/compression-aware-one-step-diffusion-model","title":"Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal","date":"2025-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jp-guo/CODiff","path":"diffusion/models/model_util.py","file_url":"https://github.com/jp-guo/CODiff/blob/HEAD/diffusion/models/model_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a9064bd19ca9f80a","mcp_get_code":{"code_sha256":"a9064bd19ca9f80a"}},{"arxiv_id":"2411.11924","paper":"/paper/dataset-distillers-are-good-label-denoisers","title":"Dataset Distillers Are Good Label Denoisers In the Wild","date":"2024-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kciiiman/dd_lnl","path":"DANCE+ours/models/resnet.py","file_url":"https://github.com/kciiiman/dd_lnl/blob/HEAD/DANCE%2Bours/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8aac35516308515a","mcp_get_code":{"code_sha256":"8aac35516308515a"}},{"arxiv_id":"2411.05361","paper":"/paper/dynamic-superb-phase-2-a-collaboratively","title":"Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks","date":"2024-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dynamic-superb/dynamic-superb","path":"api/metrics/character_error_rate.py","file_url":"https://github.com/dynamic-superb/dynamic-superb/blob/HEAD/api/metrics/character_error_rate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e3fee5065a5d1a0","mcp_get_code":{"code_sha256":"9e3fee5065a5d1a0"}},{"arxiv_id":"2410.09911","paper":"/paper/combining-generative-and-geometry-priors-for","title":"Combining Generative and Geometry Priors for Wide-Angle Portrait Correction","date":"2024-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Dev-Mrha/DualPriorsCorrection","path":"evaluate.py","file_url":"https://github.com/Dev-Mrha/DualPriorsCorrection/blob/HEAD/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7861384e46493cca","mcp_get_code":{"code_sha256":"7861384e46493cca"}},{"arxiv_id":"2410.04442","paper":"/paper/timebridge-non-stationarity-matters-for-long","title":"TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting","date":"2024-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hank0626/timebridge","path":"layers/Embed.py","file_url":"https://github.com/hank0626/timebridge/blob/HEAD/layers/Embed.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ae3eba84feb6b9e","mcp_get_code":{"code_sha256":"7ae3eba84feb6b9e"}},{"arxiv_id":"2410.02640","paper":"/paper/diffusion-based-extreme-image-compression","title":"RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huai-chang/rdeic","path":"model/rdeic.py","file_url":"https://github.com/huai-chang/rdeic/blob/HEAD/model/rdeic.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2cc202bbb038146d","mcp_get_code":{"code_sha256":"2cc202bbb038146d"}},{"arxiv_id":"2410.02604","paper":"/paper/long-sequence-recommendation-models-need","title":"Long-Sequence Recommendation Models Need Decoupled Embeddings","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuml/dare","path":"analysis/attention_accuracy_analysis/calc_gt.py","file_url":"https://github.com/thuml/dare/blob/HEAD/analysis/attention_accuracy_analysis/calc_gt.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"86c7147edb17dd5d","mcp_get_code":{"code_sha256":"86c7147edb17dd5d"}},{"arxiv_id":"2409.09144","paper":"/paper/primedepth-efficient-monocular-depth","title":"PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage","date":"2024-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vislearn/PrimeDepth","path":"ldm/modules/diffusionmodules/labeller.py","file_url":"https://github.com/vislearn/PrimeDepth/blob/HEAD/ldm/modules/diffusionmodules/labeller.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c98c9b6c537a134","mcp_get_code":{"code_sha256":"9c98c9b6c537a134"}},{"arxiv_id":"2407.14796","paper":"/paper/passion-towards-effective-incomplete-multi","title":"PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates","date":"2024-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jun-jie-shi/passion","path":"code/models/blocks.py","file_url":"https://github.com/jun-jie-shi/passion/blob/HEAD/code/models/blocks.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":"268e21ea88a8ccb1","mcp_get_code":{"code_sha256":"268e21ea88a8ccb1"}},{"arxiv_id":"2407.11633","paper":"/paper/scaling-diffusion-transformers-to-16-billion","title":"Scaling Diffusion Transformers to 16 Billion Parameters","date":"2024-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feizc/dit-moe","path":"analysis/heatmap_class.py","file_url":"https://github.com/feizc/dit-moe/blob/HEAD/analysis/heatmap_class.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"86c7147edb17dd5d","mcp_get_code":{"code_sha256":"86c7147edb17dd5d"}},{"arxiv_id":"2407.06938","paper":"/paper/rodinhd-high-fidelity-3d-avatar-generation","title":"RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models","date":"2024-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rodinhd/rodinhd","path":"pretrained_diffusion/unet.py","file_url":"https://github.com/rodinhd/rodinhd/blob/HEAD/pretrained_diffusion/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"264b320dfea0ee62","mcp_get_code":{"code_sha256":"264b320dfea0ee62"}},{"arxiv_id":"2406.01063","paper":"/paper/dance-dual-view-distribution-alignment-for","title":"DANCE: Dual-View Distribution Alignment for Dataset Condensation","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hansong-Zhang/DANCE","path":"models/resnet.py","file_url":"https://github.com/Hansong-Zhang/DANCE/blob/HEAD/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8aac35516308515a","mcp_get_code":{"code_sha256":"8aac35516308515a"}},{"arxiv_id":"2406.01063","paper":"/paper/dance-dual-view-distribution-alignment-for","title":"DANCE: Dual-View Distribution Alignment for Dataset Condensation","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hansong-Zhang/DANCE","path":"models/resnet_ap.py","file_url":"https://github.com/Hansong-Zhang/DANCE/blob/HEAD/models/resnet_ap.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8ba16d20cf03f09b","mcp_get_code":{"code_sha256":"8ba16d20cf03f09b"}},{"arxiv_id":"2405.11913","paper":"/paper/diff-bgm-a-diffusion-model-for-video","title":"Diff-BGM: A Diffusion Model for Video Background Music Generation","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sizhelee/Diff-BGM","path":"diffbgm/stable_diffusion/model/unet.py","file_url":"https://github.com/sizhelee/Diff-BGM/blob/HEAD/diffbgm/stable_diffusion/model/unet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"874ebc0bd098eb14","mcp_get_code":{"code_sha256":"874ebc0bd098eb14"}},{"arxiv_id":"2404.18820","paper":"/paper/towards-extreme-image-compression-with-latent","title":"Towards Extreme Image Compression with Latent Feature Guidance and Diffusion Prior","date":"2024-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huai-chang/DiffEIC","path":"model/diffeic.py","file_url":"https://github.com/huai-chang/DiffEIC/blob/HEAD/model/diffeic.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":"2cc202bbb038146d","mcp_get_code":{"code_sha256":"2cc202bbb038146d"}},{"arxiv_id":"2404.10297","paper":"/paper/future-language-modeling-from-temporal","title":"Future Language Modeling from Temporal Document History","date":"2024-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jlab-nlp/future-language-modeling","path":"model.py","file_url":"https://github.com/jlab-nlp/future-language-modeling/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"61513ed3f2cc3ce6","mcp_get_code":{"code_sha256":"61513ed3f2cc3ce6"}},{"arxiv_id":"2403.20126","paper":"/paper/eclipse-efficient-continual-learning-in","title":"ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clovaai/ECLIPSE","path":"continual/method_wrapper/pix_losses.py","file_url":"https://github.com/clovaai/ECLIPSE/blob/HEAD/continual/method_wrapper/pix_losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d789c5131bc6257f","mcp_get_code":{"code_sha256":"d789c5131bc6257f"}},{"arxiv_id":"2403.12931","paper":"/paper/you-only-sample-once-taming-one-step-text-to","title":"You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luo-yihong/yoso","path":"utils.py","file_url":"https://github.com/luo-yihong/yoso/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":"1524f378d161f538","mcp_get_code":{"code_sha256":"1524f378d161f538"}},{"arxiv_id":"2403.06075","paper":"/paper/multisize-dataset-condensation","title":"Multisize Dataset Condensation","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"he-y/Multisize-Dataset-Condensation","path":"models/resnet.py","file_url":"https://github.com/he-y/Multisize-Dataset-Condensation/blob/HEAD/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8aac35516308515a","mcp_get_code":{"code_sha256":"8aac35516308515a"}},{"arxiv_id":"2403.06075","paper":"/paper/multisize-dataset-condensation","title":"Multisize Dataset Condensation","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"he-y/Multisize-Dataset-Condensation","path":"models/resnet_ap.py","file_url":"https://github.com/he-y/Multisize-Dataset-Condensation/blob/HEAD/models/resnet_ap.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ba16d20cf03f09b","mcp_get_code":{"code_sha256":"8ba16d20cf03f09b"}},{"arxiv_id":"2402.18813","paper":"/paper/protein-multimer-structure-prediction-via","title":"Protein Multimer Structure Prediction via Prompt Learning","date":null,"month_inferred_from_arxiv_id":"2024-02","title_source":"archive","repo":"zqgao22/promptmsp","path":"run_pre_training.py","file_url":"https://github.com/zqgao22/promptmsp/blob/HEAD/run_pre_training.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"61e7d9ad236ec9c1","mcp_get_code":{"code_sha256":"61e7d9ad236ec9c1"}},{"arxiv_id":"2401.12708","paper":"/paper/deep-neural-network-benchmarks-for-selective","title":"Deep Neural Network Benchmarks for Selective Classification","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"andrepugni/esc","path":"classes/datasets.py","file_url":"https://github.com/andrepugni/esc/blob/HEAD/classes/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0d78ebda880af125","mcp_get_code":{"code_sha256":"0d78ebda880af125"}},{"arxiv_id":"2312.13555","paper":"/paper/cr-sam-curvature-regularized-sharpness-aware","title":"CR-SAM: Curvature Regularized Sharpness-Aware Minimization","date":"2023-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trustaiot/cr-sam","path":"metrics/hessian.py","file_url":"https://github.com/trustaiot/cr-sam/blob/HEAD/metrics/hessian.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0e4f4ed57a87f93d","mcp_get_code":{"code_sha256":"0e4f4ed57a87f93d"}},{"arxiv_id":"2312.06725","paper":"/paper/epidiff-enhancing-multi-view-synthesis-via","title":"EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion","date":"2023-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huanngzh/EpiDiff","path":"epidiff/models/blocks/resnet.py","file_url":"https://github.com/huanngzh/EpiDiff/blob/HEAD/epidiff/models/blocks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"daafc1c89511b8be","mcp_get_code":{"code_sha256":"daafc1c89511b8be"}},{"arxiv_id":"2312.02567","paper":"/paper/think-twice-before-selection-federated","title":"Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiayichen815/feal","path":"model/unet2d.py","file_url":"https://github.com/jiayichen815/feal/blob/HEAD/model/unet2d.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"159a52df66ac5d88","mcp_get_code":{"code_sha256":"159a52df66ac5d88"}},{"arxiv_id":"2311.00389","paper":"/paper/neuralgf-unsupervised-point-normal-estimation-1","title":"NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function","date":"2023-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeoQLi/NeuralGF","path":"datasets.py","file_url":"https://github.com/LeoQLi/NeuralGF/blob/HEAD/datasets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f24e8e37bec91bc7","mcp_get_code":{"code_sha256":"f24e8e37bec91bc7"}},{"arxiv_id":"2310.14019","paper":"/paper/you-only-condense-once-two-rules-for-pruning-1","title":"You Only Condense Once: Two Rules for Pruning Condensed Datasets","date":"2023-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"he-y/you-only-condense-once","path":"models/resnet.py","file_url":"https://github.com/he-y/you-only-condense-once/blob/HEAD/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8aac35516308515a","mcp_get_code":{"code_sha256":"8aac35516308515a"}},{"arxiv_id":"2310.14019","paper":"/paper/you-only-condense-once-two-rules-for-pruning-1","title":"You Only Condense Once: Two Rules for Pruning Condensed Datasets","date":"2023-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"he-y/you-only-condense-once","path":"models/resnet_ap.py","file_url":"https://github.com/he-y/you-only-condense-once/blob/HEAD/models/resnet_ap.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8ba16d20cf03f09b","mcp_get_code":{"code_sha256":"8ba16d20cf03f09b"}},{"arxiv_id":"2309.16779","paper":"/paper/intriguing-properties-of-generative","title":"Intriguing properties of generative classifiers","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SamsungSAILMontreal/ForestDiffusion","path":"gain.py","file_url":"https://github.com/SamsungSAILMontreal/ForestDiffusion/blob/HEAD/gain.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"82b1e0d73f4df6cd","mcp_get_code":{"code_sha256":"82b1e0d73f4df6cd"}},{"arxiv_id":"2309.03729","paper":"/paper/phasic-content-fusing-diffusion-model-with","title":"Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtuplayer/few-shot-diffusion","path":"model/big_unet.py","file_url":"https://github.com/sjtuplayer/few-shot-diffusion/blob/HEAD/model/big_unet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5b31715a9b105926","mcp_get_code":{"code_sha256":"5b31715a9b105926"}},{"arxiv_id":"2309.02232","paper":"/paper/fsd-an-initial-chinese-dataset-for-fake-song","title":"FSD: An Initial Chinese Dataset for Fake Song Detection","date":"2023-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xieyuankun/fsd-dataset","path":"wav2vec2_xls-r300-song/generate_FSD_online.py","file_url":"https://github.com/xieyuankun/fsd-dataset/blob/HEAD/wav2vec2_xls-r300-song/generate_FSD_online.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f1952d5b29888b39","mcp_get_code":{"code_sha256":"f1952d5b29888b39"}},{"arxiv_id":"2307.12306","paper":"/paper/tackling-the-curse-of-dimensionality-with","title":"Tackling the Curse of Dimensionality with Physics-Informed Neural Networks","date":"2023-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zheyuanhu01/SDGD_PINN","path":"Section_5.4/integration.py","file_url":"https://github.com/zheyuanhu01/SDGD_PINN/blob/HEAD/Section_5.4/integration.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0fb5012364d8f883","mcp_get_code":{"code_sha256":"0fb5012364d8f883"}},{"arxiv_id":"2307.11833","paper":"/paper/pinnsformer-a-transformer-based-framework-for","title":"PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks","date":"2023-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adityalab/pinnsformer","path":"pyhessian.py","file_url":"https://github.com/adityalab/pinnsformer/blob/HEAD/pyhessian.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0e4f4ed57a87f93d","mcp_get_code":{"code_sha256":"0e4f4ed57a87f93d"}},{"arxiv_id":"2306.09104","paper":"/paper/on-strengthening-and-defending-graph","title":"On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"86c7147edb17dd5d","mcp_get_code":{"code_sha256":"86c7147edb17dd5d"}},{"arxiv_id":"2305.16283","paper":"/paper/commonscenes-generating-commonsense-3d-indoor","title":"CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion","date":"2023-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ymxlzgy/commonscenes","path":"model/networks/diffusion_networks/sg_diff.py","file_url":"https://github.com/ymxlzgy/commonscenes/blob/HEAD/model/networks/diffusion_networks/sg_diff.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c87e65165f123ae3","mcp_get_code":{"code_sha256":"c87e65165f123ae3"}},{"arxiv_id":"2304.12891","paper":"/paper/latent-diffusion-models-for-generative","title":"Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification","date":"2023-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"meteoswiss/ldcast","path":"ldcast/models/utils.py","file_url":"https://github.com/meteoswiss/ldcast/blob/HEAD/ldcast/models/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":"9c2597fbddd2688d","mcp_get_code":{"code_sha256":"9c2597fbddd2688d"}},{"arxiv_id":"2304.12652","paper":"/paper/hybrid-neural-rendering-for-large-scale","title":"Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur","date":"2023-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CVMI-Lab/HybridNeuralRendering","path":"models/aggregators/attention.py","file_url":"https://github.com/CVMI-Lab/HybridNeuralRendering/blob/HEAD/models/aggregators/attention.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":"737aefb92460755c","mcp_get_code":{"code_sha256":"737aefb92460755c"}},{"arxiv_id":"2303.01469","paper":"/paper/consistency-models","title":"Consistency Models","date":"2023-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sainzerjj/sferd","path":"unet/student_unet.py","file_url":"https://github.com/sainzerjj/sferd/blob/HEAD/unet/student_unet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"703cead614cf486e","mcp_get_code":{"code_sha256":"703cead614cf486e"}},{"arxiv_id":"2302.06555","paper":"/paper/implications-of-the-convergence-of-language","title":"Do Vision and Language Models Share Concepts? A Vector Space Alignment Study","date":"2023-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiaangli/vlca","path":"src/utils/utils_helper.py","file_url":"https://github.com/jiaangli/vlca/blob/HEAD/src/utils/utils_helper.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":"4dc9bf9e357892c9","mcp_get_code":{"code_sha256":"4dc9bf9e357892c9"}},{"arxiv_id":"2301.05225","paper":"/paper/domain-expansion-of-image-generators","title":"Domain Expansion of Image Generators","date":"2023-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lllyasviel/controlnet","path":"cldm/cldm.py","file_url":"https://github.com/lllyasviel/controlnet/blob/HEAD/cldm/cldm.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"daafc1c89511b8be","mcp_get_code":{"code_sha256":"daafc1c89511b8be"}},{"arxiv_id":"2212.12990","paper":"/paper/unsupervised-representation-learning-from-pre","title":"Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models","date":"2022-12-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ckczzj/pdae","path":"model/shift_unet.py","file_url":"https://github.com/ckczzj/pdae/blob/HEAD/model/shift_unet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d5eb4721bd7ede86","mcp_get_code":{"code_sha256":"d5eb4721bd7ede86"}},{"arxiv_id":"2212.01241","paper":"/paper/analyzing-the-hardware-software-implications","title":"MMBench: Benchmarking End-to-End Multi-modal DNNs and Understanding Their Hardware-Software Implications","date":null,"month_inferred_from_arxiv_id":"2022-12","title_source":"archive","repo":"xfhelen/mmbench","path":"applications/Medical-Segmentation/layers.py","file_url":"https://github.com/xfhelen/mmbench/blob/HEAD/applications/Medical-Segmentation/layers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"376f134d99d8ed75","mcp_get_code":{"code_sha256":"376f134d99d8ed75"}},{"arxiv_id":"2207.13325","paper":"/paper/siri-a-simple-selective-retraining-mechanism","title":"SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual Grounding","date":"2022-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qumengxue/siri-vg","path":"models/gauss_map.py","file_url":"https://github.com/qumengxue/siri-vg/blob/HEAD/models/gauss_map.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"86c7147edb17dd5d","mcp_get_code":{"code_sha256":"86c7147edb17dd5d"}},{"arxiv_id":"2207.04655","paper":"/paper/personalizing-federated-medical-image","title":"Personalizing Federated Medical Image Segmentation via Local Calibration","date":"2022-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jcwang123/FedLC","path":"networks/lcrepnet.py","file_url":"https://github.com/jcwang123/FedLC/blob/HEAD/networks/lcrepnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"159a52df66ac5d88","mcp_get_code":{"code_sha256":"159a52df66ac5d88"}},{"arxiv_id":"2206.02425","paper":"/paper/mmformer-multimodal-medical-transformer-for","title":"mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation","date":"2022-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaozhang93/mmformer","path":"mmformer/layers.py","file_url":"https://github.com/yaozhang93/mmformer/blob/HEAD/mmformer/layers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"376f134d99d8ed75","mcp_get_code":{"code_sha256":"376f134d99d8ed75"}},{"arxiv_id":"2203.15488","paper":"/paper/over-the-air-federated-learning-via-second","title":"Over-the-Air Federated Learning via Second-Order Optimization","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Golden-Slumber/AirFL-2nd","path":"Experiments/cross_entropy_demo.py","file_url":"https://github.com/Golden-Slumber/AirFL-2nd/blob/HEAD/Experiments/cross_entropy_demo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3db4eda73fcaa60d","mcp_get_code":{"code_sha256":"3db4eda73fcaa60d"}},{"arxiv_id":"2203.01570","paper":"/paper/representing-mixtures-of-word-embeddings-with-1","title":"Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings","date":"2022-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bochengroup/wete","path":"cluster_clc.py","file_url":"https://github.com/bochengroup/wete/blob/HEAD/cluster_clc.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8e221d38153ae3cb","mcp_get_code":{"code_sha256":"8e221d38153ae3cb"}},{"arxiv_id":"2112.10752","paper":"/paper/high-resolution-image-synthesis-with-latent","title":"High-Resolution Image Synthesis with Latent Diffusion Models","date":"2021-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"labmlai/annotated_deep_learning_paper_implementations","path":"labml_nn/diffusion/stable_diffusion/model/autoencoder.py","file_url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/HEAD/labml_nn/diffusion/stable_diffusion/model/autoencoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08dcc8265812cbef","mcp_get_code":{"code_sha256":"08dcc8265812cbef"}},{"arxiv_id":"2112.10741","paper":"/paper/glide-towards-photorealistic-image-generation","title":"GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models","date":"2021-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openai/glide-text2im","path":"glide_text2im/text2im_model.py","file_url":"https://github.com/openai/glide-text2im/blob/HEAD/glide_text2im/text2im_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c93ddadd4581c6e6","mcp_get_code":{"code_sha256":"c93ddadd4581c6e6"}},{"arxiv_id":"2110.03091","paper":"/paper/improving-fractal-pre-training","title":"Improving Fractal Pre-training","date":"2021-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"catalys1/fractal-pretraining","path":"fractal_learning/training/datamodule/utils.py","file_url":"https://github.com/catalys1/fractal-pretraining/blob/HEAD/fractal_learning/training/datamodule/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f30e579115d56847","mcp_get_code":{"code_sha256":"f30e579115d56847"}},{"arxiv_id":"2104.03413","paper":"/paper/rethinking-the-backdoor-attacks-triggers-a","title":"Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective","date":"2021-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YiZeng623/frequency-backdoor","path":"Sec5_Smooth_Trigger/gauss_smooth.py","file_url":"https://github.com/YiZeng623/frequency-backdoor/blob/HEAD/Sec5_Smooth_Trigger/gauss_smooth.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df113a59813760b1","mcp_get_code":{"code_sha256":"df113a59813760b1"}},{"arxiv_id":"2008.03312","paper":"/paper/complete-parameter-inference-for-gw150914","title":"Complete parameter inference for GW150914 using deep learning","date":"2020-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stephengreen/lfi-gw","path":"lfigw/bayeswave_prior.py","file_url":"https://github.com/stephengreen/lfi-gw/blob/HEAD/lfigw/bayeswave_prior.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fab010136900c16","mcp_get_code":{"code_sha256":"4fab010136900c16"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ashwin-Pokharel/base_diffusion","path":"model.py","file_url":"https://github.com/Ashwin-Pokharel/base_diffusion/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7bab05ad7b9fa652","mcp_get_code":{"code_sha256":"7bab05ad7b9fa652"}},{"arxiv_id":"1912.07145","paper":"/paper/pyhessian-neural-networks-through-the-lens-of","title":"PyHessian: Neural Networks Through the Lens of the Hessian","date":"2019-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amirgholami/pyhessian","path":"pyhessian/utils.py","file_url":"https://github.com/amirgholami/pyhessian/blob/HEAD/pyhessian/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e4f4ed57a87f93d","mcp_get_code":{"code_sha256":"0e4f4ed57a87f93d"}},{"arxiv_id":"1905.06331","paper":"/paper/lgm-net-learning-to-generate-matching","title":"LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning","date":"2019-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"likesiwell/LGM-Net","path":"meta_matching_network.py","file_url":"https://github.com/likesiwell/LGM-Net/blob/HEAD/meta_matching_network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"79b80c56d52a1035","mcp_get_code":{"code_sha256":"79b80c56d52a1035"}},{"arxiv_id":"1904.12043","paper":"/paper/dynamic-mini-batch-sgd-for-elastic","title":"Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources","date":"2019-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AlanFokCo/compensation-tools","path":"elastic_demos/data_process.py","file_url":"https://github.com/AlanFokCo/compensation-tools/blob/HEAD/elastic_demos/data_process.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":"b98592423270ab35","mcp_get_code":{"code_sha256":"b98592423270ab35"}},{"arxiv_id":"1811.03691","paper":"/paper/can-deep-learning-outperform-modern","title":"Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?","date":"2018-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmshan/MAP-NN","path":"MAP_NN_training.py","file_url":"https://github.com/hmshan/MAP-NN/blob/HEAD/MAP_NN_training.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"44654289537e4ae8","mcp_get_code":{"code_sha256":"44654289537e4ae8"}},{"arxiv_id":"1711.00614","paper":"/paper/a-multimodal-anomaly-detector-for-robot","title":"A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder","date":"2017-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chickenbestlover/RNN-Time-series-Anomaly-Detection","path":"preprocess_data.py","file_url":"https://github.com/chickenbestlover/RNN-Time-series-Anomaly-Detection/blob/HEAD/preprocess_data.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":"ebbdec7d25441eb9","mcp_get_code":{"code_sha256":"ebbdec7d25441eb9"}},{"arxiv_id":"1508.06576","paper":"/paper/a-neural-algorithm-of-artistic-style","title":"A Neural Algorithm of Artistic Style","date":"2015-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tiannia/intro_to_ai","path":"Neural-Style-Transfer/utils.py","file_url":"https://github.com/Tiannia/intro_to_ai/blob/HEAD/Neural-Style-Transfer/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e2461546bb04850e","mcp_get_code":{"code_sha256":"e2461546bb04850e"}},{"arxiv_id":"aaai_29726","paper":null,"title":"arXiv:aaai_29726","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shesshan/CEIB","path":"utils/losses.py","file_url":"https://github.com/shesshan/CEIB/blob/HEAD/utils/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"78a0cec25e227b30","mcp_get_code":{"code_sha256":"78a0cec25e227b30"}},{"arxiv_id":"aaai_28784","paper":null,"title":"arXiv:aaai_28784","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Hansong-Zhang/M3D","path":"models/resnet.py","file_url":"https://github.com/Hansong-Zhang/M3D/blob/HEAD/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8aac35516308515a","mcp_get_code":{"code_sha256":"8aac35516308515a"}},{"arxiv_id":"aaai_28784","paper":null,"title":"arXiv:aaai_28784","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Hansong-Zhang/M3D","path":"models/resnet_ap.py","file_url":"https://github.com/Hansong-Zhang/M3D/blob/HEAD/models/resnet_ap.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ba16d20cf03f09b","mcp_get_code":{"code_sha256":"8ba16d20cf03f09b"}}]}