{"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/uniform","entry":"uniform","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":58,"n_papers_ran":18,"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":40,"n_samples_ran":16,"n_samples_fingerprinted":5,"n_places":61,"n_places_pointer_only":12,"by_status":{"ran_honours":3,"ran_violates":0,"ran_draft_wrong":9,"ran_fixture":0,"ran":4,"unverified":24},"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":"2605.20978","paper":"/paper/arxiv-2605-20978","title":"Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"hehefan/Point-Spatio-Temporal-Convolution","path":"modules/pst_convolutions.py","file_url":"https://github.com/hehefan/Point-Spatio-Temporal-Convolution/blob/HEAD/modules/pst_convolutions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2bb0eb60717758a7","mcp_get_code":{"code_sha256":"2bb0eb60717758a7"}},{"arxiv_id":"2602.03858","paper":"/paper/arxiv-2602-03858","title":"PENGUIN: General Vital Sign Reconstruction from PPG with Flow Matching State Space Model","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Neurogica/PENGUIN","path":"src/models/PENGUIN.py","file_url":"https://github.com/Neurogica/PENGUIN/blob/HEAD/src/models/PENGUIN.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause-Clear","inline_ok":false,"code_sha256_prefix":"1ca2d1ff19522291","mcp_get_code":{"code_sha256":"1ca2d1ff19522291"}},{"arxiv_id":"2601.15681","paper":"/paper/arxiv-2601-15681","title":"Consistency-Regularized GAN for Few-Shot SAR Target Recognition","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"yikuizhai/Cr-GAN","path":"crgan/losses.py","file_url":"https://github.com/yikuizhai/Cr-GAN/blob/HEAD/crgan/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b019f5a007f798da","mcp_get_code":{"code_sha256":"b019f5a007f798da"}},{"arxiv_id":"2503.16309","paper":"/paper/rapid-patient-specific-neural-networks-for","title":"Rapid patient-specific neural networks for intraoperative X-ray to volume registration","date":"2025-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eigenvivek/xvr","path":"src/xvr/model/sampler.py","file_url":"https://github.com/eigenvivek/xvr/blob/HEAD/src/xvr/model/sampler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"563c1cc6278e5940","mcp_get_code":{"code_sha256":"563c1cc6278e5940"}},{"arxiv_id":"2410.20366","paper":"/paper/rethinking-reconstruction-based-graph-level","title":"Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy","date":"2024-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7daf2f4e6899de97","mcp_get_code":{"code_sha256":"7daf2f4e6899de97"}},{"arxiv_id":"2410.20366","paper":"/paper/rethinking-reconstruction-based-graph-level","title":"Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy","date":"2024-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huggingface/open-muse","path":"muse/modeling_transformer.py","file_url":"https://github.com/huggingface/open-muse/blob/HEAD/muse/modeling_transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"66e51521e0875290","mcp_get_code":{"code_sha256":"66e51521e0875290"}},{"arxiv_id":"2410.13136","paper":"/paper/unlocking-the-capabilities-of-masked","title":"Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huggingface/open-muse","path":"muse/modeling_transformer.py","file_url":"https://github.com/huggingface/open-muse/blob/HEAD/muse/modeling_transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7daf2f4e6899de97","mcp_get_code":{"code_sha256":"7daf2f4e6899de97"}},{"arxiv_id":"2410.13136","paper":"/paper/unlocking-the-capabilities-of-masked","title":"Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiwanhur/unlockmgm","path":"muse/modeling_transformer.py","file_url":"https://github.com/jiwanhur/unlockmgm/blob/HEAD/muse/modeling_transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"66e51521e0875290","mcp_get_code":{"code_sha256":"66e51521e0875290"}},{"arxiv_id":"2409.16211","paper":"/paper/maskbit-embedding-free-image-generation-via","title":"MaskBit: Embedding-free Image Generation via Bit Tokens","date":"2024-09-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"markweberdev/maskbit","path":"evaluator/evaluator.py","file_url":"https://github.com/markweberdev/maskbit/blob/HEAD/evaluator/evaluator.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":"3695852af4744be5","mcp_get_code":{"code_sha256":"3695852af4744be5"}},{"arxiv_id":"2404.07990","paper":"/paper/openbias-open-set-bias-detection-in-text-to","title":"OpenBias: Open-set Bias Detection in Text-to-Image Generative Models","date":"2024-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"picsart-ai-research/openbias","path":"make_plots.py","file_url":"https://github.com/picsart-ai-research/openbias/blob/HEAD/make_plots.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdb9b92b20cd985b","mcp_get_code":{"code_sha256":"bdb9b92b20cd985b"}},{"arxiv_id":"2402.12535","paper":"/paper/locality-sensitive-hashing-based-efficient","title":"Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"graph-com/hept","path":"src/models/attention/hept.py","file_url":"https://github.com/graph-com/hept/blob/HEAD/src/models/attention/hept.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4a3d3d2dcce1a90a","mcp_get_code":{"code_sha256":"4a3d3d2dcce1a90a"}},{"arxiv_id":"2311.01196","paper":"/paper/combating-bilateral-edge-noise-for-robust-1","title":"Combating Bilateral Edge Noise for Robust Link Prediction","date":"2023-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"flyingdoog/PTDNet","path":"inits.py","file_url":"https://github.com/flyingdoog/PTDNet/blob/HEAD/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"2310.02821","paper":"/paper/improving-vision-anomaly-detection-with-the","title":"Improving Vision Anomaly Detection with the Guidance of Language Modality","date":"2023-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Anfeather/CMG","path":"data.py","file_url":"https://github.com/Anfeather/CMG/blob/HEAD/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a77796f33e11421d","mcp_get_code":{"code_sha256":"a77796f33e11421d"}},{"arxiv_id":"2308.09245","paper":"/paper/masked-spatio-temporal-structure-prediction","title":"Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos","date":"2023-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"johnsonsign/mast-pre","path":"modules/pst_convolutions.py","file_url":"https://github.com/johnsonsign/mast-pre/blob/HEAD/modules/pst_convolutions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2bb0eb60717758a7","mcp_get_code":{"code_sha256":"2bb0eb60717758a7"}},{"arxiv_id":"2306.03955","paper":"/paper/kernel-quadrature-with-randomly-pivoted-1","title":"Kernel quadrature with randomly pivoted Cholesky","date":"2023-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eepperly/RPCholesky-Kernel-Quadrature","path":"python/quadrature.py","file_url":"https://github.com/eepperly/RPCholesky-Kernel-Quadrature/blob/HEAD/python/quadrature.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":"1459f244b5b15490","mcp_get_code":{"code_sha256":"1459f244b5b15490"}},{"arxiv_id":"2305.02164","paper":"/paper/nonparametric-generative-modeling-with","title":"Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows","date":"2023-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"duchao0726/conditional-swf","path":"models.py","file_url":"https://github.com/duchao0726/conditional-swf/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"986a8dd38cbe6fef","mcp_get_code":{"code_sha256":"986a8dd38cbe6fef"}},{"arxiv_id":"2302.08840","paper":"/paper/learnable-topological-features-for","title":"Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks","date":"2023-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tyuxie/vbpi-sibranch","path":"src/gnn_branchModel.py","file_url":"https://github.com/tyuxie/vbpi-sibranch/blob/HEAD/src/gnn_branchModel.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"928d2600c0be05ad","mcp_get_code":{"code_sha256":"928d2600c0be05ad"}},{"arxiv_id":"2301.01456","paper":"/paper/audio-visual-efficient-conformer-for-robust","title":"Audio-Visual Efficient Conformer for Robust Speech Recognition","date":"2023-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"burchim/avec","path":"nnet/initializations.py","file_url":"https://github.com/burchim/avec/blob/HEAD/nnet/initializations.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":"d049444c78f55044","mcp_get_code":{"code_sha256":"d049444c78f55044"}},{"arxiv_id":"2301.00704","paper":"/paper/muse-text-to-image-generation-via-masked","title":"Muse: Text-To-Image Generation via Masked Generative Transformers","date":"2023-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"66e51521e0875290","mcp_get_code":{"code_sha256":"66e51521e0875290"}},{"arxiv_id":"2211.16068","paper":"/paper/ace-cooperative-multi-agent-q-learning-with","title":"ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency","date":"2022-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opendilab/ace","path":"ding/league/algorithm.py","file_url":"https://github.com/opendilab/ace/blob/HEAD/ding/league/algorithm.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":"c7dde4c97b7136b3","mcp_get_code":{"code_sha256":"c7dde4c97b7136b3"}},{"arxiv_id":"2207.06940","paper":"/paper/pasha-efficient-hpo-with-progressive-resource","title":"PASHA: Efficient HPO and NAS with Progressive Resource Allocation","date":"2022-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ondrejbohdal/pasha","path":"syne_tune/config_space.py","file_url":"https://github.com/ondrejbohdal/pasha/blob/HEAD/syne_tune/config_space.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":"5ae9da9325fddc0b","mcp_get_code":{"code_sha256":"5ae9da9325fddc0b"}},{"arxiv_id":"2207.04895","paper":"/paper/bottlenecks-club-unifying-information","title":"Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility","date":"2022-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BehroozRazeghi/CLUB","path":"utils/sampler.py","file_url":"https://github.com/BehroozRazeghi/CLUB/blob/HEAD/utils/sampler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09b4e557982cb315","mcp_get_code":{"code_sha256":"09b4e557982cb315"}},{"arxiv_id":"2206.13176","paper":"/paper/a-representation-learning-framework-for-1","title":"A Representation Learning Framework for Property Graphs","date":"2022-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yifan-h/pge","path":"pge/inits.py","file_url":"https://github.com/yifan-h/pge/blob/HEAD/pge/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"2203.04845","paper":"/paper/coarse-to-fine-sparse-transformer-for","title":"Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction","date":"2022-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caiyuanhao1998/MST","path":"real/train_code/architecture/CST.py","file_url":"https://github.com/caiyuanhao1998/MST/blob/HEAD/real/train_code/architecture/CST.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"acc89f0bd5a8ecc6","mcp_get_code":{"code_sha256":"acc89f0bd5a8ecc6"}},{"arxiv_id":"2203.03315","paper":"/paper/deep-reinforcement-learning-for-entity-1","title":"Deep Reinforcement Learning for Entity Alignment","date":"2022-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guolingbing/RLEA","path":"src/openea/approaches/gcn_align.py","file_url":"https://github.com/guolingbing/RLEA/blob/HEAD/src/openea/approaches/gcn_align.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"2111.12840","paper":"/paper/explaining-machine-learned-particle-flow","title":"Explaining machine-learned particle-flow reconstruction","date":"2021-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"faroukmokhtar/particleflow","path":"mlpf/model/hept.py","file_url":"https://github.com/faroukmokhtar/particleflow/blob/HEAD/mlpf/model/hept.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":"1d809fe0159efa28","mcp_get_code":{"code_sha256":"1d809fe0159efa28"}},{"arxiv_id":"2110.13948","paper":"/paper/boosted-cvar-classification","title":"Boosted CVaR Classification","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"runtianz/boosted_cvar","path":"algs.py","file_url":"https://github.com/runtianz/boosted_cvar/blob/HEAD/algs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f4976c92e2f0cbda","mcp_get_code":{"code_sha256":"f4976c92e2f0cbda"}},{"arxiv_id":"2106.11905","paper":"/paper/dangers-of-bayesian-model-averaging-under","title":"Dangers of Bayesian Model Averaging under Covariate Shift","date":"2021-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/google-research","path":"mir_uai24/priors.py","file_url":"https://github.com/google-research/google-research/blob/HEAD/mir_uai24/priors.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":"d942f42494790446","mcp_get_code":{"code_sha256":"d942f42494790446"}},{"arxiv_id":"2105.00266","paper":"/paper/data-driven-discovery-of-physical-laws-with","title":"Data-driven discovery of Green's functions with human-understandable deep learning","date":"2021-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NBoulle/greenlearning","path":"greenlearning/quadrature_weights.py","file_url":"https://github.com/NBoulle/greenlearning/blob/HEAD/greenlearning/quadrature_weights.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":"fc16774c82a2cb75","mcp_get_code":{"code_sha256":"fc16774c82a2cb75"}},{"arxiv_id":"2103.16026","paper":"/paper/progressively-complementary-network-for","title":"Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow","date":"2021-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uof1745-cmd/PCN","path":"FISH-Net/model/pennet4.py","file_url":"https://github.com/uof1745-cmd/PCN/blob/HEAD/FISH-Net/model/pennet4.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f21fda4691ebe44e","mcp_get_code":{"code_sha256":"f21fda4691ebe44e"}},{"arxiv_id":"2102.08850","paper":"/paper/contrastive-learning-inverts-the-data","title":"Contrastive Learning Inverts the Data Generating Process","date":"2021-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brendel-group/cl-ica","path":"main_kitti.py","file_url":"https://github.com/brendel-group/cl-ica/blob/HEAD/main_kitti.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":"efa370ec79f2dce2","mcp_get_code":{"code_sha256":"efa370ec79f2dce2"}},{"arxiv_id":"2007.09547","paper":"/paper/sat2graph-road-graph-extraction-through-graph","title":"Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding","date":"2020-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songtaohe/Sat2Graph","path":"model/tf_common_layer.py","file_url":"https://github.com/songtaohe/Sat2Graph/blob/HEAD/model/tf_common_layer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"412439874d75a376","mcp_get_code":{"code_sha256":"412439874d75a376"}},{"arxiv_id":"2006.11468","paper":"/paper/generalizing-graph-neural-networks-beyond","title":"Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs","date":"2020-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tkipf/gcn","path":"gcn/inits.py","file_url":"https://github.com/tkipf/gcn/blob/HEAD/gcn/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"2006.07331","paper":"/paper/generalized-multi-relational-graph","title":"Knowledge Embedding Based Graph Convolutional Network","date":"2020-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Maysir/GEM-GCN","path":"inits.py","file_url":"https://github.com/Maysir/GEM-GCN/blob/HEAD/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa7d8441fcd40dbf","mcp_get_code":{"code_sha256":"aa7d8441fcd40dbf"}},{"arxiv_id":"2006.06332","paper":"/paper/a-variational-approach-to-privacy-and","title":"A Variational Approach to Privacy and Fairness","date":"2020-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BehroozRazeghi/Variational-Leakage","path":"utils/sampler.py","file_url":"https://github.com/BehroozRazeghi/Variational-Leakage/blob/HEAD/utils/sampler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09b4e557982cb315","mcp_get_code":{"code_sha256":"09b4e557982cb315"}},{"arxiv_id":"2006.05806","paper":"/paper/bandit-samplers-for-training-graph-neural","title":"Bandit Samplers for Training Graph Neural Networks","date":"2020-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xavierzw/ogb-geniepath-bs","path":"inits.py","file_url":"https://github.com/xavierzw/ogb-geniepath-bs/blob/HEAD/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"2005.07227","paper":"/paper/qualitative-controller-synthesis-for","title":"Qualitative Controller Synthesis for Consumption Markov Decision Processes","date":null,"month_inferred_from_arxiv_id":"2020-05","title_source":"archive","repo":"xblahoud/FiMDP","path":"fimdp/distribution.py","file_url":"https://github.com/xblahoud/FiMDP/blob/HEAD/fimdp/distribution.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"52bf5d683852b10b","mcp_get_code":{"code_sha256":"52bf5d683852b10b"}},{"arxiv_id":"2004.01024","paper":"/paper/modeling-dynamic-heterogeneous-network-for","title":"Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN","date":"2020-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"skx300/DyHATR","path":"src/models/inits.py","file_url":"https://github.com/skx300/DyHATR/blob/HEAD/src/models/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"2002.06755","paper":"/paper/unifying-graph-convolutional-neural-networks-1","title":"Unifying Graph Convolutional Neural Networks and Label Propagation","date":"2020-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"achalagarwal/gcn-lpa","path":"src/inits.py","file_url":"https://github.com/achalagarwal/gcn-lpa/blob/HEAD/src/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5215c72b1d4d573c","mcp_get_code":{"code_sha256":"5215c72b1d4d573c"}},{"arxiv_id":"2002.06755","paper":"/paper/unifying-graph-convolutional-neural-networks-1","title":"Unifying Graph Convolutional Neural Networks and Label Propagation","date":"2020-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hwwang55/GCN-LPA","path":"src/inits.py","file_url":"https://github.com/hwwang55/GCN-LPA/blob/HEAD/src/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f433ebe52ac58c73","mcp_get_code":{"code_sha256":"f433ebe52ac58c73"}},{"arxiv_id":"1912.02908","paper":"/paper/why-having-10000-parameters-in-your-camera","title":"Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve","date":"2019-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SimonLBSoerensen/Flexible-Camera-Calibration","path":"flexiblecc/CentralModel/BSpline/knot_generators.py","file_url":"https://github.com/SimonLBSoerensen/Flexible-Camera-Calibration/blob/HEAD/flexiblecc/CentralModel/BSpline/knot_generators.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a24b649f98dd40e3","mcp_get_code":{"code_sha256":"a24b649f98dd40e3"}},{"arxiv_id":"1911.01731","paper":"/paper/graphair-graph-representation-learning-with","title":"GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction","date":"2019-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CRIPAC-DIG/GraphAIR","path":"tensorflow_code/inits.py","file_url":"https://github.com/CRIPAC-DIG/GraphAIR/blob/HEAD/tensorflow_code/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"1909.12830","paper":"/paper/the-differentiable-cross-entropy-method-1","title":"The Differentiable Cross-Entropy Method","date":"2019-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/dcem","path":"exps/cartpole_emb.py","file_url":"https://github.com/facebookresearch/dcem/blob/HEAD/exps/cartpole_emb.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4d87e0b22738782f","mcp_get_code":{"code_sha256":"4d87e0b22738782f"}},{"arxiv_id":"1908.06297","paper":"/paper/rotation-invariant-convolutions-for-3d-point","title":"Rotation Invariant Convolutions for 3D Point Clouds Deep Learning","date":"2019-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkust-vgd/riconv","path":"utils/pointfly.py","file_url":"https://github.com/hkust-vgd/riconv/blob/HEAD/utils/pointfly.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dee3f06c4723c3e4","mcp_get_code":{"code_sha256":"dee3f06c4723c3e4"}},{"arxiv_id":"1908.01491","paper":"/paper/pixel2mesh-multi-view-3d-mesh-generation-via","title":"Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation","date":"2019-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"walsvid/Pixel2MeshPlusPlus","path":"modules/inits.py","file_url":"https://github.com/walsvid/Pixel2MeshPlusPlus/blob/HEAD/modules/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"1905.13403","paper":"/paper/deep-bayesian-optimization-on-attributed","title":"Deep Bayesian Optimization on Attributed Graphs","date":"2019-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csjtx1021/DGBO","path":"gcn/inits.py","file_url":"https://github.com/csjtx1021/DGBO/blob/HEAD/gcn/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"1902.06667","paper":"/paper/semi-supervised-node-classification-via","title":"Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification","date":"2019-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CRIPAC-DIG/H-GCN","path":"tensorflow_code/inits.py","file_url":"https://github.com/CRIPAC-DIG/H-GCN/blob/HEAD/tensorflow_code/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"1901.09993","paper":"/paper/generalized-label-propagation-methods-for","title":"Label Efficient Semi-Supervised Learning via Graph Filtering","date":"2019-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liqimai/Efficient-SSL","path":"gcn/inits.py","file_url":"https://github.com/liqimai/Efficient-SSL/blob/HEAD/gcn/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"1811.11103","paper":"/paper/bayesian-graph-convolutional-neural-networks","title":"Bayesian graph convolutional neural networks for semi-supervised classification","date":"2018-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huawei-noah/BGCN","path":"src/inits.py","file_url":"https://github.com/huawei-noah/BGCN/blob/HEAD/src/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"1811.02061","paper":"/paper/a-recurrent-graph-neural-network-for-multi","title":"A Recurrent Graph Neural Network for Multi-Relational Data","date":"2018-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bioannidis/adaptive_recurrent_graph_neural_network","path":"inits.py","file_url":"https://github.com/bioannidis/adaptive_recurrent_graph_neural_network/blob/HEAD/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"1810.08575","paper":"/paper/supervising-strong-learners-by-amplifying","title":"Supervising strong learners by amplifying weak experts","date":"2018-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rmoehn/amplification","path":"amplification/tasks/core.py","file_url":"https://github.com/rmoehn/amplification/blob/HEAD/amplification/tasks/core.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"c6ae75112108e1dd","mcp_get_code":{"code_sha256":"c6ae75112108e1dd"}},{"arxiv_id":"1809.10341","paper":"/paper/deep-graph-infomax","title":"Deep Graph Infomax","date":"2018-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SpaceLearner/DisenGraphRep","path":"models/infomax.py","file_url":"https://github.com/SpaceLearner/DisenGraphRep/blob/HEAD/models/infomax.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aee2e93dc7075ef3","mcp_get_code":{"code_sha256":"aee2e93dc7075ef3"}},{"arxiv_id":"1804.01654","paper":"/paper/pixel2mesh-generating-3d-mesh-models-from","title":"Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images","date":"2018-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nywang16/Pixel2Mesh","path":"p2m/inits.py","file_url":"https://github.com/nywang16/Pixel2Mesh/blob/HEAD/p2m/inits.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":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"1804.00823","paper":"/paper/graph2seq-graph-to-sequence-learning-with","title":"Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks","date":"2018-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/Graph2Seq","path":"main/inits.py","file_url":"https://github.com/IBM/Graph2Seq/blob/HEAD/main/inits.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":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"1711.11317","paper":"/paper/unsupervised-learning-for-cell-level-visual","title":"Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks","date":"2017-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bohu615/nu_gan","path":"utils/gan_model.py","file_url":"https://github.com/bohu615/nu_gan/blob/HEAD/utils/gan_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d8786b3afb7d32a4","mcp_get_code":{"code_sha256":"d8786b3afb7d32a4"}},{"arxiv_id":"1710.10568","paper":"/paper/stochastic-training-of-graph-convolutional","title":"Stochastic Training of Graph Convolutional Networks with Variance Reduction","date":"2017-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-ml/stochastic_gcn","path":"gcn/inits.py","file_url":"https://github.com/thu-ml/stochastic_gcn/blob/HEAD/gcn/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c4b48cc045c8e12","mcp_get_code":{"code_sha256":"4c4b48cc045c8e12"}},{"arxiv_id":"1611.06740","paper":"/paper/variational-fourier-features-for-gaussian","title":"Variational Fourier features for Gaussian processes","date":"2016-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jameshensman/VFF","path":"VFF/psi_statistics.py","file_url":"https://github.com/jameshensman/VFF/blob/HEAD/VFF/psi_statistics.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":"6c664552f0a85f97","mcp_get_code":{"code_sha256":"6c664552f0a85f97"}},{"arxiv_id":"1603.08861","paper":"/paper/revisiting-semi-supervised-learning-with","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","date":"2016-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"asarigun/la-gcn-tensorflow","path":"inits.py","file_url":"https://github.com/asarigun/la-gcn-tensorflow/blob/HEAD/inits.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"aaai_20873","paper":null,"title":"arXiv:aaai_20873","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"RICE-EIC/Early-Bird-GCN","path":"gcn/inits.py","file_url":"https://github.com/RICE-EIC/Early-Bird-GCN/blob/HEAD/gcn/inits.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":"b0d1b78daecde7e9","mcp_get_code":{"code_sha256":"b0d1b78daecde7e9"}},{"arxiv_id":"aaai_17462","paper":null,"title":"arXiv:aaai_17462","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"microsoft/nni","path":"nni/parameter_expressions.py","file_url":"https://github.com/microsoft/nni/blob/HEAD/nni/parameter_expressions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"379270ae1beb4a71","mcp_get_code":{"code_sha256":"379270ae1beb4a71"}},{"arxiv_id":"Huang_Systematic_Comparison_of_Semi-supervised_and_Self-supervised_Learning_for_Medical_Image_CVPR_2024_paper","paper":null,"title":"arXiv:Huang_Systematic_Comparison_of_Semi-supervised_and_Self-supervised_Learning_for_Medical_Image_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"tufts-ml/SSL-vs-SSL-benchmark","path":"SwAV_main.py","file_url":"https://github.com/tufts-ml/SSL-vs-SSL-benchmark/blob/HEAD/SwAV_main.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0f3cbb415bf767f9","mcp_get_code":{"code_sha256":"0f3cbb415bf767f9"}}]}