{"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/integrate","entry":"integrate","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":7,"n_papers_ran":0,"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":6,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":6},"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":"2306.10161","paper":"/paper/building-the-bridge-of-schrodinger-a-1","title":"Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark","date":"2023-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ngushchin/entropicotbenchmark","path":"baselines/EntropicNeuralOptimalTransport/src/enot.py","file_url":"https://github.com/ngushchin/entropicotbenchmark/blob/HEAD/baselines/EntropicNeuralOptimalTransport/src/enot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3add5b29ad2cbd24","mcp_get_code":{"code_sha256":"3add5b29ad2cbd24"}},{"arxiv_id":"2301.13510","paper":"/paper/monocular-scene-reconstruction-with-3d-sdf","title":"3D Former: Monocular Scene Reconstruction with 3D SDF Transformers","date":"2023-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba-damo-academy/former3d","path":"former3d/tsdf_fusion.py","file_url":"https://github.com/alibaba-damo-academy/former3d/blob/HEAD/former3d/tsdf_fusion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"fecd5f220c505215","mcp_get_code":{"code_sha256":"fecd5f220c505215"}},{"arxiv_id":"2211.01156","paper":"/paper/entropic-neural-optimal-transport-via-1","title":"Entropic Neural Optimal Transport via Diffusion Processes","date":"2022-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ngushchin/entropicneuraloptimaltransport","path":"src/enot.py","file_url":"https://github.com/ngushchin/entropicneuraloptimaltransport/blob/HEAD/src/enot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3add5b29ad2cbd24","mcp_get_code":{"code_sha256":"3add5b29ad2cbd24"}},{"arxiv_id":"2205.14568","paper":"/paper/calibrated-predictive-distributions-via","title":"Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)","date":"2022-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lee-group-cmu/cal-pit","path":"src/calpit/nn/umnn/NeuralIntegral.py","file_url":"https://github.com/lee-group-cmu/cal-pit/blob/HEAD/src/calpit/nn/umnn/NeuralIntegral.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f10bf9b7644b9a05","mcp_get_code":{"code_sha256":"f10bf9b7644b9a05"}},{"arxiv_id":"2205.14568","paper":"/paper/calibrated-predictive-distributions-via","title":"Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)","date":"2022-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lee-group-cmu/cal-pit","path":"src/calpit/nn/umnn/ParallelNeuralIntegral.py","file_url":"https://github.com/lee-group-cmu/cal-pit/blob/HEAD/src/calpit/nn/umnn/ParallelNeuralIntegral.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"84a59e5fddc7a99f","mcp_get_code":{"code_sha256":"84a59e5fddc7a99f"}},{"arxiv_id":"2112.00236","paper":"/paper/vortx-volumetric-3d-reconstruction-with","title":"VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusion","date":"2021-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"noahstier/vortx","path":"vortx/tsdf_fusion.py","file_url":"https://github.com/noahstier/vortx/blob/HEAD/vortx/tsdf_fusion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fecd5f220c505215","mcp_get_code":{"code_sha256":"fecd5f220c505215"}},{"arxiv_id":"1805.07411","paper":"/paper/discovery-of-nonlinear-multiscale-systems","title":"Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings","date":null,"month_inferred_from_arxiv_id":"2018-05","title_source":"archive","repo":"kpchamp/MultiscaleDiscovery","path":"utils/utils.py","file_url":"https://github.com/kpchamp/MultiscaleDiscovery/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"51ec96927f21771b","mcp_get_code":{"code_sha256":"51ec96927f21771b"}},{"arxiv_id":"1703.04247","paper":"/paper/deepfm-a-factorization-machine-based-neural","title":"DeepFM: A Factorization-Machine based Neural Network for CTR Prediction","date":"2017-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baiyimeng/umc","path":"calib/ParallelNeuralIntegral.py","file_url":"https://github.com/baiyimeng/umc/blob/HEAD/calib/ParallelNeuralIntegral.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"152b5034b4ef794e","mcp_get_code":{"code_sha256":"152b5034b4ef794e"}}]}