{"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/conv2d-2","entry":"Conv2d","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":43,"n_papers_ran":38,"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":47,"n_samples_ran":42,"n_samples_fingerprinted":33,"n_places":47,"n_places_pointer_only":23,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":41,"unverified":5},"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":"2604.09041","paper":"/paper/arxiv-2604-09041","title":"U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"Rose-STL-Lab/u-cast","path":"src/models/networks_edm.py","file_url":"https://github.com/Rose-STL-Lab/u-cast/blob/HEAD/src/models/networks_edm.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":"bc9ac84ffbec918e","mcp_get_code":{"code_sha256":"bc9ac84ffbec918e"}},{"arxiv_id":"2512.15657","paper":"/paper/arxiv-2512-15657","title":"SoFlow: Solution Flow Models for One-Step Generative Modeling","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"zlab-princeton/SoFlow","path":"models.py","file_url":"https://github.com/zlab-princeton/SoFlow/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba2131e39006711e","mcp_get_code":{"code_sha256":"ba2131e39006711e"}},{"arxiv_id":"2512.00252","paper":"/paper/arxiv-2512-00252","title":"DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"Erik-Wikingsson/DAISI","path":"assimilation/methods/daisi.py","file_url":"https://github.com/Erik-Wikingsson/DAISI/blob/HEAD/assimilation/methods/daisi.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3665e6132b89d21e","mcp_get_code":{"code_sha256":"3665e6132b89d21e"}},{"arxiv_id":"2510.12479","paper":"/paper/arxiv-2510-12479","title":"MH-LVC: Multi-Hypothesis Temporal Prediction for Learned Conditional Residual Video Coding","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"NYCU-MAPL/MHLVC","path":"compressai/RIFE/FFNet.py","file_url":"https://github.com/NYCU-MAPL/MHLVC/blob/HEAD/compressai/RIFE/FFNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00ee54a5dde8ef25","mcp_get_code":{"code_sha256":"00ee54a5dde8ef25"}},{"arxiv_id":"2510.06691","paper":"/paper/arxiv-2510-06691","title":"Latent Representation Learning in Heavy-Ion Collisions with MaskPoint Transformer","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"Giovanni-Sforza/MaskPoint-AMPT","path":"models/MaskPoint.py","file_url":"https://github.com/Giovanni-Sforza/MaskPoint-AMPT/blob/HEAD/models/MaskPoint.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"64ccb0213c04a3a6","mcp_get_code":{"code_sha256":"64ccb0213c04a3a6"}},{"arxiv_id":"2503.12897","paper":"/paper/federated-continual-instruction-tuning","title":"Federated Continual Instruction Tuning","date":"2025-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ghy0501/FCIT","path":"CoIN/peft/tuners/coinmoelora.py","file_url":"https://github.com/Ghy0501/FCIT/blob/HEAD/CoIN/peft/tuners/coinmoelora.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":"e55b3841b7343a24","mcp_get_code":{"code_sha256":"e55b3841b7343a24"}},{"arxiv_id":"2409.13803","paper":"/paper/intrinsic-single-image-hdr-reconstruction","title":"Intrinsic Single-Image HDR Reconstruction","date":"2024-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"compphoto/IntrinsicHDR","path":"src/model.py","file_url":"https://github.com/compphoto/IntrinsicHDR/blob/HEAD/src/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"cfc4ea99518b923d","mcp_get_code":{"code_sha256":"cfc4ea99518b923d"}},{"arxiv_id":"2408.10159","paper":"/paper/customizing-language-models-with-instance","title":"Customizing Language Models with Instance-wise LoRA for Sequential Recommendation","date":"2024-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akalikong/ilora","path":"model/peft/tuners/moelora.py","file_url":"https://github.com/akalikong/ilora/blob/HEAD/model/peft/tuners/moelora.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9d19ea5c94bfb203","mcp_get_code":{"code_sha256":"9d19ea5c94bfb203"}},{"arxiv_id":"2407.16448","paper":"/paper/monowad-weather-adaptive-diffusion-model-for","title":"MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisualAIKHU/MonoWAD","path":"visualDet3D/networks/detectors/MonoWAD.py","file_url":"https://github.com/VisualAIKHU/MonoWAD/blob/HEAD/visualDet3D/networks/detectors/MonoWAD.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f54fd544c431a89","mcp_get_code":{"code_sha256":"9f54fd544c431a89"}},{"arxiv_id":"2406.07083","paper":"/paper/efficient-mixture-learning-in-black-box","title":"Efficient Mixture Learning in Black-Box Variational Inference","date":"2024-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"okviman/efficient-mixtures","path":"models/misvae.py","file_url":"https://github.com/okviman/efficient-mixtures/blob/HEAD/models/misvae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ed3b70ab7a6d4b5d","mcp_get_code":{"code_sha256":"ed3b70ab7a6d4b5d"}},{"arxiv_id":"2405.16273","paper":"/paper/m-3-gpt-an-advanced-multimodal-multitask","title":"M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation","date":"2024-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luomingshuang/m3gpt","path":"m3gpt/core/models/decoders/network/transformer_decoder/transformer_decoder.py","file_url":"https://github.com/luomingshuang/m3gpt/blob/HEAD/m3gpt/core/models/decoders/network/transformer_decoder/transformer_decoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"af4cc52db9e5aa3b","mcp_get_code":{"code_sha256":"af4cc52db9e5aa3b"}},{"arxiv_id":"2403.03077","paper":"/paper/mikasa-multi-key-anchor-scene-aware","title":"MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dfki-av/mikasa-3dvg","path":"models/MiKASA_transformer.py","file_url":"https://github.com/dfki-av/mikasa-3dvg/blob/HEAD/models/MiKASA_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"43954295982678e1","mcp_get_code":{"code_sha256":"43954295982678e1"}},{"arxiv_id":"2402.13717","paper":"/paper/neeko-leveraging-dynamic-lora-for-efficient","title":"Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weiyifan1023/neeko","path":"moelora/tuners/moelora.py","file_url":"https://github.com/weiyifan1023/neeko/blob/HEAD/moelora/tuners/moelora.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":"08a41b3367d5a129","mcp_get_code":{"code_sha256":"08a41b3367d5a129"}},{"arxiv_id":"2401.01552","paper":"/paper/cra-pcn-point-cloud-completion-with-intra-and","title":"CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers","date":"2024-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EasyRy/CRA-PCN","path":"models/crapcn.py","file_url":"https://github.com/EasyRy/CRA-PCN/blob/HEAD/models/crapcn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"111d7d0d3df6bada","mcp_get_code":{"code_sha256":"111d7d0d3df6bada"}},{"arxiv_id":"2310.13545","paper":"/paper/scalelong-towards-more-stable-training-of-1","title":"ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection","date":"2023-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YichengDWu/tinyedm","path":"src/tinyedm/networks.py","file_url":"https://github.com/YichengDWu/tinyedm/blob/HEAD/src/tinyedm/networks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c4d769802a65492","mcp_get_code":{"code_sha256":"2c4d769802a65492"}},{"arxiv_id":"2307.14008","paper":"/paper/adaptive-frequency-filters-as-efficient","title":"Adaptive Frequency Filters As Efficient Global Token Mixers","date":"2023-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NWPU-Li/AFFNet","path":"aff_block_LL.py","file_url":"https://github.com/NWPU-Li/AFFNet/blob/HEAD/aff_block_LL.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"95bbde412a6c735c","mcp_get_code":{"code_sha256":"95bbde412a6c735c"}},{"arxiv_id":"2305.08293","paper":"/paper/identity-preserving-talking-face-generation","title":"Identity-Preserving Talking Face Generation with Landmark and Appearance Priors","date":"2023-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Weizhi-Zhong/IP_LAP","path":"models/landmark_generator.py","file_url":"https://github.com/Weizhi-Zhong/IP_LAP/blob/HEAD/models/landmark_generator.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":"64c4ace973c8c4a6","mcp_get_code":{"code_sha256":"64c4ace973c8c4a6"}},{"arxiv_id":"2210.04458","paper":"/paper/ogc-unsupervised-3d-object-segmentation-from","title":"OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds","date":"2022-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vlar-group/ogc","path":"models/segnet_ogcdr.py","file_url":"https://github.com/vlar-group/ogc/blob/HEAD/models/segnet_ogcdr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"66590da846c5ae6d","mcp_get_code":{"code_sha256":"66590da846c5ae6d"}},{"arxiv_id":"2207.10188","paper":"/paper/bitwidth-adaptive-quantization-aware-neural","title":"Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach","date":"2022-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jsjs0369/MEBQAT","path":"code/few_shot/mebqat_maml/arch.py","file_url":"https://github.com/jsjs0369/MEBQAT/blob/HEAD/code/few_shot/mebqat_maml/arch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41d957993fab1fb","mcp_get_code":{"code_sha256":"d41d957993fab1fb"}},{"arxiv_id":"2206.08545","paper":"/paper/nu-wave-2-a-general-neural-audio-upsampling","title":"NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates","date":"2022-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mindslab-ai/nuwave2","path":"model.py","file_url":"https://github.com/mindslab-ai/nuwave2/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"f3aede0f86dce25f","mcp_get_code":{"code_sha256":"f3aede0f86dce25f"}},{"arxiv_id":"2204.07346","paper":"/paper/mvster-epipolar-transformer-for-efficient","title":"MVSTER: Epipolar Transformer for Efficient Multi-View Stereo","date":"2022-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JeffWang987/MVSTER","path":"models/MVS4Net.py","file_url":"https://github.com/JeffWang987/MVSTER/blob/HEAD/models/MVS4Net.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3aafeb0680c73abf","mcp_get_code":{"code_sha256":"3aafeb0680c73abf"}},{"arxiv_id":"2202.04901","paper":"/paper/film-frame-interpolation-for-large-motion","title":"FILM: Frame Interpolation for Large Motion","date":"2022-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dajes/frame-interpolation-pytorch","path":"interpolator.py","file_url":"https://github.com/dajes/frame-interpolation-pytorch/blob/HEAD/interpolator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"33031ed5f24825b1","mcp_get_code":{"code_sha256":"33031ed5f24825b1"}},{"arxiv_id":"2201.04850","paper":"/paper/bridgeformer-bridging-video-text-retrieval","title":"Bridging Video-text Retrieval with Multiple Choice Questions","date":"2022-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"towhee-io/towhee","path":"towhee/models/bridgeformer/bridge_former_training_block.py","file_url":"https://github.com/towhee-io/towhee/blob/HEAD/towhee/models/bridgeformer/bridge_former_training_block.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"cf6f823dcb3ac547","mcp_get_code":{"code_sha256":"cf6f823dcb3ac547"}},{"arxiv_id":"2112.05999","paper":"/paper/curvature-guided-dynamic-scale-networks-for-1","title":"Curvature-guided dynamic scale networks for Multi-view Stereo","date":"2021-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"truongkhang/cds-mvsnet","path":"models/model.py","file_url":"https://github.com/truongkhang/cds-mvsnet/blob/HEAD/models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3bc61cf3e881f9be","mcp_get_code":{"code_sha256":"3bc61cf3e881f9be"}},{"arxiv_id":"2110.08787","paper":"/paper/pixelpyramids-exact-inference-models-from-1","title":"PixelPyramids: Exact Inference Models from Lossless Image Pyramids","date":"2021-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visinf/pixelpyramids","path":"unet/pixelcnnpp.py","file_url":"https://github.com/visinf/pixelpyramids/blob/HEAD/unet/pixelcnnpp.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":"6387ca84ef0e560e","mcp_get_code":{"code_sha256":"6387ca84ef0e560e"}},{"arxiv_id":"2109.09881","paper":"/paper/estimating-and-exploiting-the-aleatoric","title":"Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation","date":"2021-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baegwangbin/surface_normal_uncertainty","path":"models/submodules/decoder.py","file_url":"https://github.com/baegwangbin/surface_normal_uncertainty/blob/HEAD/models/submodules/decoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f091ebf994fa95e4","mcp_get_code":{"code_sha256":"f091ebf994fa95e4"}},{"arxiv_id":"2108.10904","paper":"/paper/simvlm-simple-visual-language-model","title":"SimVLM: Simple Visual Language Model Pretraining with Weak Supervision","date":"2021-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FerryHuang/SimVLM","path":"simvlm/modeling_simvlm.py","file_url":"https://github.com/FerryHuang/SimVLM/blob/HEAD/simvlm/modeling_simvlm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"15258dcfb20b4488","mcp_get_code":{"code_sha256":"15258dcfb20b4488"}},{"arxiv_id":"2108.09551","paper":"/paper/variable-rate-deep-image-compression-through","title":"Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform","date":"2021-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"micmic123/qmapcompression","path":"models/models.py","file_url":"https://github.com/micmic123/qmapcompression/blob/HEAD/models/models.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":"57912304b19e055b","mcp_get_code":{"code_sha256":"57912304b19e055b"}},{"arxiv_id":"2108.07009","paper":"/paper/pixel-difference-networks-for-efficient-edge","title":"Pixel Difference Networks for Efficient Edge Detection","date":"2021-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuoinoulu/pidinet","path":"models/pidinet.py","file_url":"https://github.com/zhuoinoulu/pidinet/blob/HEAD/models/pidinet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"026700536ed63347","mcp_get_code":{"code_sha256":"026700536ed63347"}},{"arxiv_id":"2108.04444","paper":"/paper/snowflakenet-point-cloud-completion-by","title":"SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer","date":"2021-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenxiangx/snowflakenet","path":"models/model_completion.py","file_url":"https://github.com/allenxiangx/snowflakenet/blob/HEAD/models/model_completion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2f63e227a64d6745","mcp_get_code":{"code_sha256":"2f63e227a64d6745"}},{"arxiv_id":"2106.09685","paper":"/paper/lora-low-rank-adaptation-of-large-language","title":"LoRA: Low-Rank Adaptation of Large Language Models","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhanglab-aim/lingo","path":"peftnew/tuners/lora.py","file_url":"https://github.com/zhanglab-aim/lingo/blob/HEAD/peftnew/tuners/lora.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"4025b3aa55f8b3e1","mcp_get_code":{"code_sha256":"4025b3aa55f8b3e1"}},{"arxiv_id":"2106.09685","paper":"/paper/lora-low-rank-adaptation-of-large-language","title":"LoRA: Low-Rank Adaptation of Large Language Models","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"phoebussi/alpaca-cot","path":"peft/src/peft/tuners/lora.py","file_url":"https://github.com/phoebussi/alpaca-cot/blob/HEAD/peft/src/peft/tuners/lora.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f156ba83a62bfd88","mcp_get_code":{"code_sha256":"f156ba83a62bfd88"}},{"arxiv_id":"2106.04779","paper":"/paper/point-cloud-upsampling-via-disentangled","title":"Point Cloud Upsampling via Disentangled Refinement","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryanphilly/DIS-PU-pytorch","path":"dispu/generator.py","file_url":"https://github.com/ryanphilly/DIS-PU-pytorch/blob/HEAD/dispu/generator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b0d00cfbeccb5778","mcp_get_code":{"code_sha256":"b0d00cfbeccb5778"}},{"arxiv_id":"2103.14030","paper":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"towhee-io/towhee","path":"towhee/models/swin_transformer/model.py","file_url":"https://github.com/towhee-io/towhee/blob/HEAD/towhee/models/swin_transformer/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"18583975af70b0aa","mcp_get_code":{"code_sha256":"18583975af70b0aa"}},{"arxiv_id":"2007.15627","paper":"/paper/unsupervised-continuous-object-representation","title":"Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision","date":"2020-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nicolaihaeni/corn","path":"models/layers/blocks.py","file_url":"https://github.com/nicolaihaeni/corn/blob/HEAD/models/layers/blocks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6c0ebe78ed7f17f","mcp_get_code":{"code_sha256":"e6c0ebe78ed7f17f"}},{"arxiv_id":"2007.09200","paper":"/paper/neural-networks-with-recurrent-generative","title":"Neural Networks with Recurrent Generative Feedback","date":"2020-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yjhuangcd/CNNF","path":"cnnf/layers.py","file_url":"https://github.com/yjhuangcd/CNNF/blob/HEAD/cnnf/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"291afb8a874ef9f3","mcp_get_code":{"code_sha256":"291afb8a874ef9f3"}},{"arxiv_id":"2006.05467","paper":"/paper/pruning-neural-networks-without-any-data-by","title":"Pruning neural networks without any data by iteratively conserving synaptic flow","date":"2020-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iurada/px-ntk-pruning","path":"lib/pruners.py","file_url":"https://github.com/iurada/px-ntk-pruning/blob/HEAD/lib/pruners.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d526bce01cf543a6","mcp_get_code":{"code_sha256":"d526bce01cf543a6"}},{"arxiv_id":"2002.11896","paper":"/paper/gradient-boosted-flows","title":"Gradient Boosted Normalizing Flows","date":"2020-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"robert-giaquinto/gradient-boosted-normalizing-flows","path":"models/boosted_flow.py","file_url":"https://github.com/robert-giaquinto/gradient-boosted-normalizing-flows/blob/HEAD/models/boosted_flow.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2d7d6fd92e4989a5","mcp_get_code":{"code_sha256":"2d7d6fd92e4989a5"}},{"arxiv_id":"1903.01864","paper":"/paper/frustum-convnet-sliding-frustums-to-aggregate","title":"Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection","date":"2019-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhixinwang/frustum-convnet","path":"models/common.py","file_url":"https://github.com/zhixinwang/frustum-convnet/blob/HEAD/models/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce58b42def83bfb4","mcp_get_code":{"code_sha256":"ce58b42def83bfb4"}},{"arxiv_id":"1812.04948","paper":"/paper/a-style-based-generator-architecture-for","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","date":"2018-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ialhashim/StyleGAN-Tensorflow2","path":"stylegan.py","file_url":"https://github.com/ialhashim/StyleGAN-Tensorflow2/blob/HEAD/stylegan.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"2e76d2c6f82316e7","mcp_get_code":{"code_sha256":"2e76d2c6f82316e7"}},{"arxiv_id":"1812.04948","paper":"/paper/a-style-based-generator-architecture-for","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","date":"2018-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"podgorskiy/StyleGAN_Blobless","path":"net.py","file_url":"https://github.com/podgorskiy/StyleGAN_Blobless/blob/HEAD/net.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"013751315bd5d53c","mcp_get_code":{"code_sha256":"013751315bd5d53c"}},{"arxiv_id":"1807.03039","paper":"/paper/glow-generative-flow-with-invertible-1x1","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","date":"2018-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"y0ast/Glow-PyTorch","path":"model.py","file_url":"https://github.com/y0ast/Glow-PyTorch/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d15a5ce5143a7f33","mcp_get_code":{"code_sha256":"d15a5ce5143a7f33"}},{"arxiv_id":"1706.02413","paper":"/paper/pointnet-deep-hierarchical-feature-learning","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","date":"2017-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyang-ur/SAT","path":"referit3d/external_tools/pointnet2/pointnet2_modules.py","file_url":"https://github.com/zyang-ur/SAT/blob/HEAD/referit3d/external_tools/pointnet2/pointnet2_modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"196a75317b3cf7d1","mcp_get_code":{"code_sha256":"196a75317b3cf7d1"}},{"arxiv_id":"1706.02413","paper":"/paper/pointnet-deep-hierarchical-feature-learning","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","date":"2017-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tao-tao-tao-tao-tao/diffusion_suction","path":"train/models/backbone2/pointnet2/pointnet2_backbone.py","file_url":"https://github.com/tao-tao-tao-tao-tao/diffusion_suction/blob/HEAD/train/models/backbone2/pointnet2/pointnet2_backbone.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"13cd8bfa92565fb3","mcp_get_code":{"code_sha256":"13cd8bfa92565fb3"}},{"arxiv_id":"1706.02413","paper":"/paper/pointnet-deep-hierarchical-feature-learning","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","date":"2017-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"referit3d/referit3d","path":"referit3d/models/backbone/point_net_pp.py","file_url":"https://github.com/referit3d/referit3d/blob/HEAD/referit3d/models/backbone/point_net_pp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"11786f1c29af0e47","mcp_get_code":{"code_sha256":"11786f1c29af0e47"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dkalpakchi/ReproducingSCAPytorch","path":"few_shot_learning_system.py","file_url":"https://github.com/dkalpakchi/ReproducingSCAPytorch/blob/HEAD/few_shot_learning_system.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0978ebdb0ce3ed2","mcp_get_code":{"code_sha256":"b0978ebdb0ce3ed2"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sk1123344/U-2-NET-and-U-NET","path":"unet.py","file_url":"https://github.com/sk1123344/U-2-NET-and-U-NET/blob/HEAD/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"218d84bfc034b381","mcp_get_code":{"code_sha256":"218d84bfc034b381"}}]}