{"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/geglu-2","entry":"geglu","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":13,"n_papers_ran":13,"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":4,"n_samples_fingerprinted":3,"n_places":15,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":1,"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":"2601.22816","paper":"/paper/arxiv-2601-22816","title":"Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"yandex-research/tab-ddpm","path":"tab_ddpm/modules.py","file_url":"https://github.com/yandex-research/tab-ddpm/blob/HEAD/tab_ddpm/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"49f8fe0655f00ee5","mcp_get_code":{"code_sha256":"49f8fe0655f00ee5"}},{"arxiv_id":"2504.04798","paper":"/paper/tabrep-a-simple-and-effective-continuous","title":"TabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion Models","date":"2025-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jacobyhsi/TabRep","path":"tabrep_ddpm/models/modules.py","file_url":"https://github.com/jacobyhsi/TabRep/blob/HEAD/tabrep_ddpm/models/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"49f8fe0655f00ee5","mcp_get_code":{"code_sha256":"49f8fe0655f00ee5"}},{"arxiv_id":"2504.04798","paper":"/paper/tabrep-a-simple-and-effective-continuous","title":"TabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion Models","date":"2025-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jacobyhsi/TabRep","path":"tabrep_flow/models/modules.py","file_url":"https://github.com/jacobyhsi/TabRep/blob/HEAD/tabrep_flow/models/modules.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3971992cf9c5dcfc","mcp_get_code":{"code_sha256":"3971992cf9c5dcfc"}},{"arxiv_id":"2405.20690","paper":"/paper/unleashing-the-potential-of-diffusion-models","title":"Unleashing the Potential of Diffusion Models for Incomplete Data Imputation","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hengruizhang98/DiffPuter","path":"model.py","file_url":"https://github.com/hengruizhang98/DiffPuter/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"49f8fe0655f00ee5","mcp_get_code":{"code_sha256":"49f8fe0655f00ee5"}},{"arxiv_id":"2403.01570","paper":"/paper/serval-synergy-learning-between-vertical","title":"SERVAL: Synergy Learning between Vertical Models and LLMs towards Oracle-Level Zero-shot Medical Prediction","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jyansir/sersal","path":"models/t2g.py","file_url":"https://github.com/jyansir/sersal/blob/HEAD/models/t2g.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":"d7bace13fb369137","mcp_get_code":{"code_sha256":"d7bace13fb369137"}},{"arxiv_id":"2402.06806","paper":"/paper/towards-principled-assessment-of-tabular-data","title":"Systematic Assessment of Tabular Data Synthesis Algorithms","date":"2024-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zealscott/synmeter","path":"synthesizer/ddpm/modules.py","file_url":"https://github.com/zealscott/synmeter/blob/HEAD/synthesizer/ddpm/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"49f8fe0655f00ee5","mcp_get_code":{"code_sha256":"49f8fe0655f00ee5"}},{"arxiv_id":"2402.04538","paper":"/paper/triplet-interaction-improves-graph","title":"Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers","date":"2024-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shamim-hussain/tgt","path":"lib/tgt/layers/activations.py","file_url":"https://github.com/shamim-hussain/tgt/blob/HEAD/lib/tgt/layers/activations.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46e9996c80897eae","mcp_get_code":{"code_sha256":"46e9996c80897eae"}},{"arxiv_id":"2312.17679","paper":"/paper/data-augmentation-for-supervised-graph","title":"Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models","date":"2023-12-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kayzliu/godm","path":"godm/diffusion.py","file_url":"https://github.com/kayzliu/godm/blob/HEAD/godm/diffusion.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"49f8fe0655f00ee5","mcp_get_code":{"code_sha256":"49f8fe0655f00ee5"}},{"arxiv_id":"2309.16220","paper":"/paper/unmasking-the-chameleons-a-benchmark-for-out","title":"Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mazizmalayeri/tabmedood","path":"models/predictive_models.py","file_url":"https://github.com/mazizmalayeri/tabmedood/blob/HEAD/models/predictive_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"647ed25965ec25b1","mcp_get_code":{"code_sha256":"647ed25965ec25b1"}},{"arxiv_id":"2211.16887","paper":"/paper/t2g-former-organizing-tabular-features-into","title":"T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction","date":"2022-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jyansir/t2g-former","path":"lib/deep.py","file_url":"https://github.com/jyansir/t2g-former/blob/HEAD/lib/deep.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":"d7bace13fb369137","mcp_get_code":{"code_sha256":"d7bace13fb369137"}},{"arxiv_id":"2209.15421","paper":"/paper/tabddpm-modelling-tabular-data-with-diffusion","title":"TabDDPM: Modelling Tabular Data with Diffusion Models","date":"2022-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rotot0/tab-ddpm","path":"tab_ddpm/modules.py","file_url":"https://github.com/rotot0/tab-ddpm/blob/HEAD/tab_ddpm/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"49f8fe0655f00ee5","mcp_get_code":{"code_sha256":"49f8fe0655f00ee5"}},{"arxiv_id":"2207.03208","paper":"/paper/revisiting-pretraining-objectives-for-tabular","title":"Revisiting Pretraining Objectives for Tabular Deep Learning","date":"2022-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kalelpark/DeepLearning-for-Tabular-Data","path":"model/fttransformer.py","file_url":"https://github.com/kalelpark/DeepLearning-for-Tabular-Data/blob/HEAD/model/fttransformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d7bace13fb369137","mcp_get_code":{"code_sha256":"d7bace13fb369137"}},{"arxiv_id":"2207.03208","paper":"/paper/revisiting-pretraining-objectives-for-tabular","title":"Revisiting Pretraining Objectives for Tabular Deep Learning","date":"2022-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kalelpark/DeepLearning-for-Tabular-Data","path":"model/common.py","file_url":"https://github.com/kalelpark/DeepLearning-for-Tabular-Data/blob/HEAD/model/common.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":"267506bc9a077f62","mcp_get_code":{"code_sha256":"267506bc9a077f62"}},{"arxiv_id":"2106.11959","paper":"/paper/revisiting-deep-learning-models-for-tabular","title":"Revisiting Deep Learning Models for Tabular Data","date":"2021-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Yura52/tabular-dl-revisiting-models","path":"lib/deep.py","file_url":"https://github.com/Yura52/tabular-dl-revisiting-models/blob/HEAD/lib/deep.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d7bace13fb369137","mcp_get_code":{"code_sha256":"d7bace13fb369137"}},{"arxiv_id":"2102.08921","paper":"/paper/how-faithful-is-your-synthetic-data-sample","title":"How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models","date":"2021-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fangzy96/tabcutmix","path":"tabsyn/model.py","file_url":"https://github.com/fangzy96/tabcutmix/blob/HEAD/tabsyn/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"49f8fe0655f00ee5","mcp_get_code":{"code_sha256":"49f8fe0655f00ee5"}}]}