{"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/fit","entry":"fit","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":23,"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":23,"n_samples_ran":5,"n_samples_fingerprinted":0,"n_places":24,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":2,"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":"2607.16987","paper":"/paper/arxiv-2607-16987","title":"Twisted Schrödinger Bridge Matching","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"maxencenoble/twisted-sb-matching","path":"bridge/spline/gaussian_path.py","file_url":"https://github.com/maxencenoble/twisted-sb-matching/blob/HEAD/bridge/spline/gaussian_path.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a6ce05c38a2af82","mcp_get_code":{"code_sha256":"2a6ce05c38a2af82"}},{"arxiv_id":"2602.20833","paper":"/paper/arxiv-2602-20833","title":"DRESS: A Continuous Framework for Structural Graph Refinement","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"velicast/dress-graph","path":"python/src/dress/core.py","file_url":"https://github.com/velicast/dress-graph/blob/HEAD/python/src/dress/core.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2902b27b6eb35ebe","mcp_get_code":{"code_sha256":"2902b27b6eb35ebe"}},{"arxiv_id":"2505.04608","paper":"/paper/watch-weighted-adaptive-testing-for","title":"WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales","date":"2025-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aaronhan223/watch","path":"src/main_pod_ram.py","file_url":"https://github.com/aaronhan223/watch/blob/HEAD/src/main_pod_ram.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":"3dcf309104240174","mcp_get_code":{"code_sha256":"3dcf309104240174"}},{"arxiv_id":"2503.18066","paper":"/paper/accurate-peak-detection-in-multimodal","title":"Accurate Peak Detection in Multimodal Optimization via Approximated Landscape Learning","date":"2025-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gmc-drl/apdmmo","path":"surrogate.py","file_url":"https://github.com/gmc-drl/apdmmo/blob/HEAD/surrogate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"8131b3f5abb83bbf","mcp_get_code":{"code_sha256":"8131b3f5abb83bbf"}},{"arxiv_id":"2411.12925","paper":"/paper/loss-to-loss-prediction-scaling-laws-for-all","title":"Loss-to-Loss Prediction: Scaling Laws for All Datasets","date":"2024-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KempnerInstitute/loss-to-loss-notebooks","path":"utils_scaling.py","file_url":"https://github.com/KempnerInstitute/loss-to-loss-notebooks/blob/HEAD/utils_scaling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f916802c9c8b374e","mcp_get_code":{"code_sha256":"f916802c9c8b374e"}},{"arxiv_id":"2410.13831","paper":"/paper/the-disparate-benefits-of-deep-ensembles","title":"The Disparate Benefits of Deep Ensembles","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-jku/disparate-benefits","path":"source/utils/train_utils.py","file_url":"https://github.com/ml-jku/disparate-benefits/blob/HEAD/source/utils/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd461a45f2667e8c","mcp_get_code":{"code_sha256":"dd461a45f2667e8c"}},{"arxiv_id":"2410.03074","paper":"/paper/metaood-automatic-selection-of-ood-detection","title":"MetaOOD: Automatic Selection of OOD Detection Models","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yqin43/metaood","path":"metaood/metaood_0.py","file_url":"https://github.com/yqin43/metaood/blob/HEAD/metaood/metaood_0.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ee6c756d2eb41474","mcp_get_code":{"code_sha256":"ee6c756d2eb41474"}},{"arxiv_id":"2409.12067","paper":"/paper/fitting-multilevel-factor-models","title":"Fitting Multilevel Factor Models","date":"2024-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvxgrp/multilevel_factor_model","path":"mfmodel/fast_em_algorithm.py","file_url":"https://github.com/cvxgrp/multilevel_factor_model/blob/HEAD/mfmodel/fast_em_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":"97c969723353df2a","mcp_get_code":{"code_sha256":"97c969723353df2a"}},{"arxiv_id":"2407.07003","paper":"/paper/learning-to-complement-and-to-defer-to","title":"Learning to Complement and to Defer to Multiple Users","date":"2024-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengzhang37/lecodu","path":"utils/tools.py","file_url":"https://github.com/zhengzhang37/lecodu/blob/HEAD/utils/tools.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cf3259099e459c51","mcp_get_code":{"code_sha256":"cf3259099e459c51"}},{"arxiv_id":"2311.16054","paper":"/paper/metric-space-magnitude-for-evaluating","title":"Metric Space Magnitude for Evaluating the Diversity of Latent Representations","date":"2023-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"renata-turkes/turkevs2022on","path":"SRC/model.py","file_url":"https://github.com/renata-turkes/turkevs2022on/blob/HEAD/SRC/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e95cd27c390703a","mcp_get_code":{"code_sha256":"9e95cd27c390703a"}},{"arxiv_id":"2306.11925","paper":"/paper/lvm-med-learning-large-scale-self-supervised-1","title":"LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching","date":"2023-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"duyhominhnguyen/LVM-Med","path":"segmentation_2d/LVMMed_SAM_2d.py","file_url":"https://github.com/duyhominhnguyen/LVM-Med/blob/HEAD/segmentation_2d/LVMMed_SAM_2d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5da7a0e57d058010","mcp_get_code":{"code_sha256":"5da7a0e57d058010"}},{"arxiv_id":"2302.05828","paper":"/paper/graph-neural-network-inspired-kernels-for","title":"Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning","date":"2023-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"niuzehao/gnn-gp","path":"src/predict.py","file_url":"https://github.com/niuzehao/gnn-gp/blob/HEAD/src/predict.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":"1d4c96cb8248baa4","mcp_get_code":{"code_sha256":"1d4c96cb8248baa4"}},{"arxiv_id":"2208.12104","paper":"/paper/algorithmic-differentiation-for-automatized","title":"Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields","date":"2022-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"niklasschmitz/ad-kernels","path":"md17/experiments/hyper_coulomb.py","file_url":"https://github.com/niklasschmitz/ad-kernels/blob/HEAD/md17/experiments/hyper_coulomb.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ff7a01e3fef14913","mcp_get_code":{"code_sha256":"ff7a01e3fef14913"}},{"arxiv_id":"2111.14874","paper":"/paper/weighing-the-milky-way-and-andromeda-with","title":"Weighing the Milky Way and Andromeda with Artificial Intelligence","date":"2021-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PabloVD/HaloGraphNet","path":"camelsplots.py","file_url":"https://github.com/PabloVD/HaloGraphNet/blob/HEAD/camelsplots.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d91b96b668aca991","mcp_get_code":{"code_sha256":"d91b96b668aca991"}},{"arxiv_id":"2107.08189","paper":"/paper/beds-bench-behavior-of-ehr-models-under","title":"BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark","date":"2021-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-health/records-research","path":"beds-bench/models/GP.py","file_url":"https://github.com/google-health/records-research/blob/HEAD/beds-bench/models/GP.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a16ad595e3803161","mcp_get_code":{"code_sha256":"a16ad595e3803161"}},{"arxiv_id":"2010.12644","paper":"/paper/a-biologically-plausible-neural-network-for-1","title":"A biologically plausible neural network for Slow Feature Analysis","date":"2020-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"flatironinstitute/bio-sfa","path":"biosfa.py","file_url":"https://github.com/flatironinstitute/bio-sfa/blob/HEAD/biosfa.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bf634500932d3ad4","mcp_get_code":{"code_sha256":"bf634500932d3ad4"}},{"arxiv_id":"2007.05721","paper":"/paper/towards-robust-classification-with-deep","title":"Towards Robust Classification with Deep Generative Forests","date":"2020-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AlCorreia/GeFs","path":"gefs/learning.py","file_url":"https://github.com/AlCorreia/GeFs/blob/HEAD/gefs/learning.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9e0d52c893cf2bb","mcp_get_code":{"code_sha256":"e9e0d52c893cf2bb"}},{"arxiv_id":"2006.12982","paper":"/paper/disentangling-by-subspace-diffusion","title":"Disentangling by Subspace Diffusion","date":"2020-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepmind/deepmind-research","path":"geomancer/geomancer.py","file_url":"https://github.com/deepmind/deepmind-research/blob/HEAD/geomancer/geomancer.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":"ab9c21314cb23449","mcp_get_code":{"code_sha256":"ab9c21314cb23449"}},{"arxiv_id":"1910.09457","paper":"/paper/aleatoric-and-epistemic-uncertainty-in","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","date":"2019-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tfmortie/uaml","path":"uaml/process.py","file_url":"https://github.com/tfmortie/uaml/blob/HEAD/uaml/process.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e274e4d018b72bba","mcp_get_code":{"code_sha256":"e274e4d018b72bba"}},{"arxiv_id":"1809.03672","paper":"/paper/deep-interest-evolution-network-for-click","title":"Deep Interest Evolution Network for Click-Through Rate Prediction","date":"2018-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GitHub-HongweiZhang/prediction-flow","path":"prediction_flow/pytorch/functions.py","file_url":"https://github.com/GitHub-HongweiZhang/prediction-flow/blob/HEAD/prediction_flow/pytorch/functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44610d575b31e33c","mcp_get_code":{"code_sha256":"44610d575b31e33c"}},{"arxiv_id":"1803.01837","paper":"/paper/st-gan-spatial-transformer-generative","title":"ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing","date":"2018-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenhsuanlin/spatial-transformer-GAN","path":"glasses/warp.py","file_url":"https://github.com/chenhsuanlin/spatial-transformer-GAN/blob/HEAD/glasses/warp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5dfbbadd25cd659e","mcp_get_code":{"code_sha256":"5dfbbadd25cd659e"}},{"arxiv_id":"1612.03897","paper":"/paper/inverse-compositional-spatial-transformer","title":"Inverse Compositional Spatial Transformer Networks","date":"2016-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenhsuanlin/inverse-compositional-STN","path":"MNIST-tensorflow/warp.py","file_url":"https://github.com/chenhsuanlin/inverse-compositional-STN/blob/HEAD/MNIST-tensorflow/warp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5dfbbadd25cd659e","mcp_get_code":{"code_sha256":"5dfbbadd25cd659e"}},{"arxiv_id":"1612.03897","paper":"/paper/inverse-compositional-spatial-transformer","title":"Inverse Compositional Spatial Transformer Networks","date":"2016-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenhsuanlin/inverse-compositional-STN","path":"MNIST-pytorch/warp.py","file_url":"https://github.com/chenhsuanlin/inverse-compositional-STN/blob/HEAD/MNIST-pytorch/warp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"91f76d01aa3d7d32","mcp_get_code":{"code_sha256":"91f76d01aa3d7d32"}},{"arxiv_id":"1606.01865","paper":"/paper/recurrent-neural-networks-for-multivariate","title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values","date":"2016-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fteufel/PyTorch-GRU-D","path":"scripts/run_gru_d.py","file_url":"https://github.com/fteufel/PyTorch-GRU-D/blob/HEAD/scripts/run_gru_d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"79e0b1b1652e2e27","mcp_get_code":{"code_sha256":"79e0b1b1652e2e27"}}]}