{"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/inverse-softplus","entry":"inverse_softplus","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":8,"n_papers_ran":5,"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":7,"n_samples_ran":5,"n_samples_fingerprinted":5,"n_places":8,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":2,"unverified":2},"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":"2606.26273","paper":"/paper/arxiv-2606-26273","title":"Equivariance and Augmentation for Bayesian Neural Networks","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"dmw1998/augment-BNNs","path":"ebnn/symmetrize.py","file_url":"https://github.com/dmw1998/augment-BNNs/blob/HEAD/ebnn/symmetrize.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3331cc72b05316bd","mcp_get_code":{"code_sha256":"3331cc72b05316bd"}},{"arxiv_id":"2601.10690","paper":"/paper/arxiv-2601-10690","title":"Data-driven stochastic reduced-order modeling of parametrized dynamical systems","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"ailersic/rom-visde","path":"visde/kernel.py","file_url":"https://github.com/ailersic/rom-visde/blob/HEAD/visde/kernel.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"ad8c62159a7582fd","mcp_get_code":{"code_sha256":"ad8c62159a7582fd"}},{"arxiv_id":"2508.04929","paper":"/paper/arxiv-2508-04929","title":"CryoSplat: Gaussian Splatting for Cryo-EM Homogeneous Reconstruction","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"Chen-Suyi/cryosplat","path":"gaussian_model.py","file_url":"https://github.com/Chen-Suyi/cryosplat/blob/HEAD/gaussian_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"a7a044d72ceede8d","mcp_get_code":{"code_sha256":"a7a044d72ceede8d"}},{"arxiv_id":"2411.16443","paper":"/paper/splatflow-multi-view-rectified-flow-model-for","title":"SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis","date":"2024-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gohyojun15/SplatFlow","path":"model/refiner/gs_util.py","file_url":"https://github.com/gohyojun15/SplatFlow/blob/HEAD/model/refiner/gs_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e5e924570703db04","mcp_get_code":{"code_sha256":"e5e924570703db04"}},{"arxiv_id":"2406.17601","paper":"/paper/director3d-real-world-camera-trajectory-and","title":"Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"imlixinyang/director3d","path":"modules/refiners/gs_utils.py","file_url":"https://github.com/imlixinyang/director3d/blob/HEAD/modules/refiners/gs_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e5e924570703db04","mcp_get_code":{"code_sha256":"e5e924570703db04"}},{"arxiv_id":"2405.20693","paper":"/paper/r-2-gaussian-rectifying-radiative-gaussian","title":"R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ruyi-Zha/r2_gaussian","path":"r2_gaussian/utils/gaussian_utils.py","file_url":"https://github.com/Ruyi-Zha/r2_gaussian/blob/HEAD/r2_gaussian/utils/gaussian_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"124130db4f645e1c","mcp_get_code":{"code_sha256":"124130db4f645e1c"}},{"arxiv_id":"2312.06518","paper":"/paper/decoupling-meta-reinforcement-learning-with","title":"Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills","date":"2023-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hehongc/DCMRL","path":"simpl/math.py","file_url":"https://github.com/hehongc/DCMRL/blob/HEAD/simpl/math.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18e1435aa3a7c7f5","mcp_get_code":{"code_sha256":"18e1435aa3a7c7f5"}},{"arxiv_id":"2105.14594","paper":"/paper/sparse-uncertainty-representation-in-deep-1","title":"Sparse Uncertainty Representation in Deep Learning with Inducing Weights","date":"2021-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/bayesianize","path":"bnn/nn/mixins/variational/inducing.py","file_url":"https://github.com/microsoft/bayesianize/blob/HEAD/bnn/nn/mixins/variational/inducing.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a62afa5b91e4a9b","mcp_get_code":{"code_sha256":"9a62afa5b91e4a9b"}}]}