{"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/dense-2","entry":"dense","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":22,"n_papers_ran":4,"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":22,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":24,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":3,"unverified":18},"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":"2606.17249","paper":"/paper/arxiv-2606-17249","title":"From Compression to Deployment: Real-Time and Energy-Efficient FastGRNN on Ultra-Constrained Microcontrollers","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"emre1998/fastgrnn-har","path":"analyze_ablation.py","file_url":"https://github.com/emre1998/fastgrnn-har/blob/HEAD/analyze_ablation.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":"0341425cb85fcd56","mcp_get_code":{"code_sha256":"0341425cb85fcd56"}},{"arxiv_id":"2606.12896","paper":"/paper/arxiv-2606-12896","title":"PolicyGuard: Towards Test-time and Step-level Adversary (Backdoor) Defense for Reinforcement Learning Agent","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"openai/multiagent-competition","path":"policy.py","file_url":"https://github.com/openai/multiagent-competition/blob/HEAD/policy.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f4281d13f703e4b3","mcp_get_code":{"code_sha256":"f4281d13f703e4b3"}},{"arxiv_id":"2508.19857","paper":"/paper/arxiv-2508-19857","title":"Quantum latent distributions in deep generative models","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"NVlabs/denoising-diffusion-gan","path":"score_sde/models/dense_layer.py","file_url":"https://github.com/NVlabs/denoising-diffusion-gan/blob/HEAD/score_sde/models/dense_layer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"9f13fd20494eac47","mcp_get_code":{"code_sha256":"9f13fd20494eac47"}},{"arxiv_id":"2405.10531","paper":"/paper/nonparametric-teaching-of-implicit-neural","title":"Nonparametric Teaching of Implicit Neural Representations","date":"2024-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chen2hang/INT_NonparametricTeaching","path":"src/strategy.py","file_url":"https://github.com/chen2hang/INT_NonparametricTeaching/blob/HEAD/src/strategy.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36ecff07553664f4","mcp_get_code":{"code_sha256":"36ecff07553664f4"}},{"arxiv_id":"2404.18185","paper":"/paper/ranked-list-truncation-for-large-language","title":"Ranked List Truncation for Large Language Model-based Re-Ranking","date":"2024-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chuanmeng/rlt4reranking","path":"rlt/features.py","file_url":"https://github.com/chuanmeng/rlt4reranking/blob/HEAD/rlt/features.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fd2589a2883b6137","mcp_get_code":{"code_sha256":"fd2589a2883b6137"}},{"arxiv_id":"2403.19898","paper":"/paper/structure-matters-tackling-the-semantic","title":"Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"htyjers/StrDiffusion","path":"train/discriminator/config/inpainting/models/dense_layer.py","file_url":"https://github.com/htyjers/StrDiffusion/blob/HEAD/train/discriminator/config/inpainting/models/dense_layer.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":"9f13fd20494eac47","mcp_get_code":{"code_sha256":"9f13fd20494eac47"}},{"arxiv_id":"2303.05456","paper":"/paper/restoration-based-generative-models","title":"Restoration based Generative Models","date":"2023-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jae-Moo/RGM","path":"models/dense_layer.py","file_url":"https://github.com/Jae-Moo/RGM/blob/HEAD/models/dense_layer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f13fd20494eac47","mcp_get_code":{"code_sha256":"9f13fd20494eac47"}},{"arxiv_id":"2109.14120","paper":"/paper/meta-learning-on-a-sequence-of-imbalanced","title":"Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness","date":"2021-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joey-wang123/imbalancemeta","path":"net/convnet.py","file_url":"https://github.com/joey-wang123/imbalancemeta/blob/HEAD/net/convnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"79289153d293347d","mcp_get_code":{"code_sha256":"79289153d293347d"}},{"arxiv_id":"2103.12266","paper":"/paper/deep-implicit-moving-least-squares-functions","title":"Deep Implicit Moving Least-Squares Functions for 3D Reconstruction","date":"2021-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Andy97/DeepMLS","path":"Octree/ocnn.py","file_url":"https://github.com/Andy97/DeepMLS/blob/HEAD/Octree/ocnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"025d5ba4076f4507","mcp_get_code":{"code_sha256":"025d5ba4076f4507"}},{"arxiv_id":"2010.07717","paper":"/paper/wasserstein-distance-regularized-sequence","title":"Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains","date":"2020-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RUC-WSM/WD-Match","path":"src/model.py","file_url":"https://github.com/RUC-WSM/WD-Match/blob/HEAD/src/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2d4063ceaaf34982","mcp_get_code":{"code_sha256":"2d4063ceaaf34982"}},{"arxiv_id":"2007.10323","paper":"/paper/pillar-based-object-detection-for-autonomous","title":"Pillar-based Object Detection for Autonomous Driving","date":"2020-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WangYueFt/pillar-od","path":"network.py","file_url":"https://github.com/WangYueFt/pillar-od/blob/HEAD/network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b1471476d6fe5102","mcp_get_code":{"code_sha256":"b1471476d6fe5102"}},{"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":"CW-Huang/sdeflow-light","path":"lib/models/unet.py","file_url":"https://github.com/CW-Huang/sdeflow-light/blob/HEAD/lib/models/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":"5aa603017b7bf9da","mcp_get_code":{"code_sha256":"5aa603017b7bf9da"}},{"arxiv_id":"2002.09741","paper":"/paper/vflow-more-expressive-generative-flows-with","title":"VFlow: More Expressive Generative Flows with Variational Data Augmentation","date":"2020-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-ml/vflow","path":"flows/flows.py","file_url":"https://github.com/thu-ml/vflow/blob/HEAD/flows/flows.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9bab91e30fb7e13f","mcp_get_code":{"code_sha256":"9bab91e30fb7e13f"}},{"arxiv_id":"1910.14192","paper":"/paper/transferable-end-to-end-aspect-based","title":"Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning","date":"2019-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hsqmlzno1/Transferable-E2E-ABSA","path":"models/nn_utils.py","file_url":"https://github.com/hsqmlzno1/Transferable-E2E-ABSA/blob/HEAD/models/nn_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ffb732a897de4191","mcp_get_code":{"code_sha256":"ffb732a897de4191"}},{"arxiv_id":"1909.02177","paper":"/paper/neural-rule-grounding-for-low-resource","title":"NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction","date":"2019-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"INK-USC/NERO","path":"func.py","file_url":"https://github.com/INK-USC/NERO/blob/HEAD/func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aecd65a13632edca","mcp_get_code":{"code_sha256":"aecd65a13632edca"}},{"arxiv_id":"1902.00275","paper":"/paper/flow-improving-flow-based-generative-models","title":"Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design","date":"2019-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aravind0706/flowpp","path":"flows/flows.py","file_url":"https://github.com/aravind0706/flowpp/blob/HEAD/flows/flows.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c31d5dfdb4e6308","mcp_get_code":{"code_sha256":"7c31d5dfdb4e6308"}},{"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":"LignumResearch/stylewood-model-usage","path":"training/networks_progan.py","file_url":"https://github.com/LignumResearch/stylewood-model-usage/blob/HEAD/training/networks_progan.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ae13612dd3d28384","mcp_get_code":{"code_sha256":"ae13612dd3d28384"}},{"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":"comp-imaging-sci/pic-recon","path":"stylegan2/training/networks_stylegan.py","file_url":"https://github.com/comp-imaging-sci/pic-recon/blob/HEAD/stylegan2/training/networks_stylegan.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3265555a15861717","mcp_get_code":{"code_sha256":"3265555a15861717"}},{"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":"perplexingpegasus/EarthLandscapeGAN","path":"models/stylegan.py","file_url":"https://github.com/perplexingpegasus/EarthLandscapeGAN/blob/HEAD/models/stylegan.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2695e06f765e3af9","mcp_get_code":{"code_sha256":"2695e06f765e3af9"}},{"arxiv_id":"1812.02288","paper":"/paper/adversarially-learned-anomaly-detection","title":"Adversarially Learned Anomaly Detection","date":"2018-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zahradehghanian97/rcalad","path":"utils/sn.py","file_url":"https://github.com/zahradehghanian97/rcalad/blob/HEAD/utils/sn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c49a28c165a5fb0d","mcp_get_code":{"code_sha256":"c49a28c165a5fb0d"}},{"arxiv_id":"1804.05862","paper":"/paper/non-vacuous-generalization-bounds-at-the","title":"Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach","date":"2018-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wendazhou/nnet-compression-generalization","path":"nnet/models/layers.py","file_url":"https://github.com/wendazhou/nnet-compression-generalization/blob/HEAD/nnet/models/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d3ddbb49f3799d81","mcp_get_code":{"code_sha256":"d3ddbb49f3799d81"}},{"arxiv_id":"1705.05363","paper":"/paper/curiosity-driven-exploration-by-self","title":"Curiosity-driven Exploration by Self-supervised Prediction","date":"2017-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alex-petrenko/curious-rl","path":"algorithms/curious_a2c/agent_curious_a2c.py","file_url":"https://github.com/alex-petrenko/curious-rl/blob/HEAD/algorithms/curious_a2c/agent_curious_a2c.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae87872e3b4545e5","mcp_get_code":{"code_sha256":"ae87872e3b4545e5"}},{"arxiv_id":"1409.1556","paper":"/paper/very-deep-convolutional-networks-for-large","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","date":"2014-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pat-coady/tiny_imagenet","path":"src/vgg_16.py","file_url":"https://github.com/pat-coady/tiny_imagenet/blob/HEAD/src/vgg_16.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"38dffbce9ad2acdd","mcp_get_code":{"code_sha256":"38dffbce9ad2acdd"}},{"arxiv_id":"1406.5298","paper":"/paper/semi-supervised-learning-with-deep-generative-1","title":"Semi-Supervised Learning with Deep Generative Models","date":"2014-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trungnt13/odin-ai","path":"odin/networks/resnets.py","file_url":"https://github.com/trungnt13/odin-ai/blob/HEAD/odin/networks/resnets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"658e0ef1022fbe20","mcp_get_code":{"code_sha256":"658e0ef1022fbe20"}}]}