{"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/rmse-2","entry":"RMSE","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":15,"n_papers_ran":12,"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":14,"n_samples_ran":10,"n_samples_fingerprinted":9,"n_places":17,"n_places_pointer_only":8,"by_status":{"ran_honours":2,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":7,"unverified":4},"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":"2604.27967","paper":"/paper/arxiv-2604-27967","title":"Differentiable latent structure discovery for interpretable forecasting in clinical time series","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"yalavarthivk/GraFITi","path":"train_grafiti.py","file_url":"https://github.com/yalavarthivk/GraFITi/blob/HEAD/train_grafiti.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"48c047f5225a3300","mcp_get_code":{"code_sha256":"48c047f5225a3300"}},{"arxiv_id":"2604.27967","paper":"/paper/arxiv-2604-27967","title":"Differentiable latent structure discovery for interpretable forecasting in clinical time series","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"yalavarthivk/GraFITi","path":"baseline_experiments/train_linodenet.py","file_url":"https://github.com/yalavarthivk/GraFITi/blob/HEAD/baseline_experiments/train_linodenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2ba5cb1a7937a316","mcp_get_code":{"code_sha256":"2ba5cb1a7937a316"}},{"arxiv_id":"2501.00089","paper":"/paper/insights-on-galaxy-evolution-from","title":"Insights on Galaxy Evolution from Interpretable Sparse Feature Networks","date":"2024-12-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jwuphysics/sparse-feature-networks","path":"src/trainer.py","file_url":"https://github.com/jwuphysics/sparse-feature-networks/blob/HEAD/src/trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0df193a10e00aec6","mcp_get_code":{"code_sha256":"0df193a10e00aec6"}},{"arxiv_id":"2407.07564","paper":"/paper/trainable-highly-expressive-activation","title":"Trainable Highly-expressive Activation Functions","date":"2024-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bgu-cs-vil/ditac","path":"regression_example/evaluation.py","file_url":"https://github.com/bgu-cs-vil/ditac/blob/HEAD/regression_example/evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0088fd84030dee57","mcp_get_code":{"code_sha256":"0088fd84030dee57"}},{"arxiv_id":"2406.11244","paper":"/paper/spot-mamba-learning-long-range-dependency-on","title":"SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces","date":"2024-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bdi-lab/spot-mamba","path":"utils/metrics.py","file_url":"https://github.com/bdi-lab/spot-mamba/blob/HEAD/utils/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"602913d134538363","mcp_get_code":{"code_sha256":"602913d134538363"}},{"arxiv_id":"2405.10800","paper":"/paper/heterogeneity-informed-meta-parameter","title":"Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting","date":"2024-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xdzhelheim/himnet","path":"lib/metrics.py","file_url":"https://github.com/xdzhelheim/himnet/blob/HEAD/lib/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"602913d134538363","mcp_get_code":{"code_sha256":"602913d134538363"}},{"arxiv_id":"2402.02005","paper":"/paper/topology-informed-graph-transformer","title":"Topology-Informed Graph Transformer","date":"2024-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leemingo/cy2mixer","path":"lib/metrics.py","file_url":"https://github.com/leemingo/cy2mixer/blob/HEAD/lib/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"602913d134538363","mcp_get_code":{"code_sha256":"602913d134538363"}},{"arxiv_id":"2311.10580","paper":"/paper/implicit-maximum-a-posteriori-filtering-via","title":"Implicit Maximum a Posteriori Filtering via Adaptive Optimization","date":"2023-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gianlucabencomo/implicitmap","path":"nonlinear/helper.py","file_url":"https://github.com/gianlucabencomo/implicitmap/blob/HEAD/nonlinear/helper.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b43a661b7945075a","mcp_get_code":{"code_sha256":"b43a661b7945075a"}},{"arxiv_id":"2311.03721","paper":"/paper/climateset-a-large-scale-climate-model","title":"ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RolnickLab/ClimateSet","path":"emulator/src/core/metrics.py","file_url":"https://github.com/RolnickLab/ClimateSet/blob/HEAD/emulator/src/core/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"d53ef8ed07875379","mcp_get_code":{"code_sha256":"d53ef8ed07875379"}},{"arxiv_id":"2310.17911","paper":null,"title":"arXiv:2310.17911","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":null,"repo":"hyperspectral-skin/Hyper-Skin-2023","path":"helpers/losses.py","file_url":"https://github.com/hyperspectral-skin/Hyper-Skin-2023/blob/HEAD/helpers/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c876160b1995e7d5","mcp_get_code":{"code_sha256":"c876160b1995e7d5"}},{"arxiv_id":"2302.10363","paper":"/paper/transformed-distribution-matching-for-missing","title":"Transformed Distribution Matching for Missing Value Imputation","date":"2023-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hezgit/tdm","path":"tdm.py","file_url":"https://github.com/hezgit/tdm/blob/HEAD/tdm.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"67a88f0d09f0e59e","mcp_get_code":{"code_sha256":"67a88f0d09f0e59e"}},{"arxiv_id":"2206.06565","paper":"/paper/lift-language-interfaced-fine-tuning-for-non","title":"LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks","date":"2022-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uw-madison-lee-lab/languageinterfacedfinetuning","path":"regression/utils/GPTJFineTuner.py","file_url":"https://github.com/uw-madison-lee-lab/languageinterfacedfinetuning/blob/HEAD/regression/utils/GPTJFineTuner.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c9db8e7368f3b29","mcp_get_code":{"code_sha256":"4c9db8e7368f3b29"}},{"arxiv_id":"2202.11678","paper":"/paper/bayesian-model-selection-the-marginal","title":"Bayesian Model Selection, the Marginal Likelihood, and Generalization","date":"2022-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sanaelotfi/bayesian_model_comparison","path":"DKL_experiments/exact_runner.py","file_url":"https://github.com/sanaelotfi/bayesian_model_comparison/blob/HEAD/DKL_experiments/exact_runner.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"93fe84b4567ceb63","mcp_get_code":{"code_sha256":"93fe84b4567ceb63"}},{"arxiv_id":"2201.12886","paper":"/paper/n-hits-neural-hierarchical-interpolation-for","title":"N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting","date":"2022-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eeci/annex_37","path":"assess_forecasts.py","file_url":"https://github.com/eeci/annex_37/blob/HEAD/assess_forecasts.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"200be6d2c9f9dfff","mcp_get_code":{"code_sha256":"200be6d2c9f9dfff"}},{"arxiv_id":"1411.2005","paper":"/paper/scalable-variational-gaussian-process","title":"Scalable Variational Gaussian Process Classification","date":"2014-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ppsp-team/PyNM","path":"pynm/util.py","file_url":"https://github.com/ppsp-team/PyNM/blob/HEAD/pynm/util.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":"7ab6ad702c247369","mcp_get_code":{"code_sha256":"7ab6ad702c247369"}},{"arxiv_id":"aaai_25976","paper":null,"title":"arXiv:aaai_25976","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"deepkashiwa20/MegaCRN","path":"model/metrics.py","file_url":"https://github.com/deepkashiwa20/MegaCRN/blob/HEAD/model/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"602913d134538363","mcp_get_code":{"code_sha256":"602913d134538363"}},{"arxiv_id":"aaai_25976","paper":null,"title":"arXiv:aaai_25976","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"deepkashiwa20/MegaCRN","path":"model_EXPYTKY/metrics.py","file_url":"https://github.com/deepkashiwa20/MegaCRN/blob/HEAD/model_EXPYTKY/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"10ccb12f6ac2f295","mcp_get_code":{"code_sha256":"10ccb12f6ac2f295"}}]}