{"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/train-val-test-split","entry":"train_val_test_split","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":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":13,"n_samples_ran":5,"n_samples_fingerprinted":0,"n_places":15,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":4,"unverified":8},"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":"2603.23016","paper":"/paper/arxiv-2603-23016","title":"A Sobering Look at Tabular Data Generation via Probabilistic Circuits","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"april-tools/tabpc","path":"datasets_scripts/tabdiff_process_dataset.py","file_url":"https://github.com/april-tools/tabpc/blob/HEAD/datasets_scripts/tabdiff_process_dataset.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":"e034defca8827e6c","mcp_get_code":{"code_sha256":"e034defca8827e6c"}},{"arxiv_id":"2502.01171","paper":"/paper/efficient-and-scalable-density-functional","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/sphnet","path":"src/training/utils.py","file_url":"https://github.com/microsoft/sphnet/blob/HEAD/src/training/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"451a8000566c7c43","mcp_get_code":{"code_sha256":"451a8000566c7c43"}},{"arxiv_id":"2410.20626","paper":"/paper/tabdiff-a-multi-modal-diffusion-model-for","title":"TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation","date":"2024-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"minkaixu/tabdiff","path":"process_dataset.py","file_url":"https://github.com/minkaixu/tabdiff/blob/HEAD/process_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e034defca8827e6c","mcp_get_code":{"code_sha256":"e034defca8827e6c"}},{"arxiv_id":"2404.17169","paper":"/paper/fairgt-a-fairness-aware-graph-transformer","title":"FairGT: A Fairness-aware Graph Transformer","date":"2024-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yushuowiki/fairgt","path":"utils.py","file_url":"https://github.com/yushuowiki/fairgt/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5e16588d4905b5fe","mcp_get_code":{"code_sha256":"5e16588d4905b5fe"}},{"arxiv_id":"2311.18639","paper":"/paper/targeted-reduction-of-causal-models","title":"Targeted Reduction of Causal Models","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akekic/targeted-causal-reduction","path":"targeted_causal_reduction/data_generators/processing/core.py","file_url":"https://github.com/akekic/targeted-causal-reduction/blob/HEAD/targeted_causal_reduction/data_generators/processing/core.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"57a92be91162a459","mcp_get_code":{"code_sha256":"57a92be91162a459"}},{"arxiv_id":"2310.03240","paper":"/paper/relational-convolutional-networks-a-framework","title":"Learning Hierarchical Relational Representations through Relational Convolutions","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awni00/relational-convolutions","path":"utils.py","file_url":"https://github.com/awni00/relational-convolutions/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8f28cfccddd15816","mcp_get_code":{"code_sha256":"8f28cfccddd15816"}},{"arxiv_id":"2307.10683","paper":"/paper/fractional-denoising-for-3d-molecular-pre","title":"Fractional Denoising for 3D Molecular Pre-training","date":"2023-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fengshikun/frad","path":"torchmdnet/utils.py","file_url":"https://github.com/fengshikun/frad/blob/HEAD/torchmdnet/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9b9f6689028801f7","mcp_get_code":{"code_sha256":"9b9f6689028801f7"}},{"arxiv_id":"2303.08951","paper":"/paper/the-tiny-time-series-transformer-low-latency","title":"The Tiny Time-series Transformer: Low-latency High-throughput Classification of Astronomical Transients using Deep Model Compression","date":"2023-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tallamjr/astronet","path":"astronet/utils.py","file_url":"https://github.com/tallamjr/astronet/blob/HEAD/astronet/utils.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":"99fc3ce23c4cb98b","mcp_get_code":{"code_sha256":"99fc3ce23c4cb98b"}},{"arxiv_id":"2206.00133","paper":"/paper/pre-training-via-denoising-for-molecular","title":"Pre-training via Denoising for Molecular Property Prediction","date":"2022-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shehzaidi/pre-training-via-denoising","path":"torchmdnet/utils.py","file_url":"https://github.com/shehzaidi/pre-training-via-denoising/blob/HEAD/torchmdnet/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a633af654f04a42","mcp_get_code":{"code_sha256":"6a633af654f04a42"}},{"arxiv_id":"2009.12710","paper":"/paper/heterogeneous-molecular-graph-neural-networks","title":"Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties","date":"2020-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shuix007/HMGNN","path":"Preprocess.py","file_url":"https://github.com/shuix007/HMGNN/blob/HEAD/Preprocess.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c49e32e26635d7a8","mcp_get_code":{"code_sha256":"c49e32e26635d7a8"}},{"arxiv_id":"2004.05150","paper":"/paper/longformer-the-long-document-transformer","title":"Longformer: The Long-Document Transformer","date":"2020-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaketae/pytorch-malware-detection","path":"src/deep_malware_detection/dataset.py","file_url":"https://github.com/jaketae/pytorch-malware-detection/blob/HEAD/src/deep_malware_detection/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e71dea05795c03bf","mcp_get_code":{"code_sha256":"e71dea05795c03bf"}},{"arxiv_id":"1710.09435","paper":"/paper/malware-detection-by-eating-a-whole-exe","title":"Malware Detection by Eating a Whole EXE","date":"2017-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaketae/deep-malware-detection","path":"src/deep_malware_detection/dataset.py","file_url":"https://github.com/jaketae/deep-malware-detection/blob/HEAD/src/deep_malware_detection/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e71dea05795c03bf","mcp_get_code":{"code_sha256":"e71dea05795c03bf"}},{"arxiv_id":"1612.09106","paper":"/paper/sequence-to-point-learning-with-neural","title":"Sequence-to-point learning with neural networks for nonintrusive load monitoring","date":"2016-12-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"redefik/ConvNILM","path":"data_preprocessing.py","file_url":"https://github.com/redefik/ConvNILM/blob/HEAD/data_preprocessing.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc452ed44ce06dea","mcp_get_code":{"code_sha256":"fc452ed44ce06dea"}},{"arxiv_id":"ijcai2025_0872","paper":null,"title":"arXiv:ijcai2025_0872","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HongxinXiang/EDG","path":"EDG-for-VisNet/visnet_for_EDG/utils.py","file_url":"https://github.com/HongxinXiang/EDG/blob/HEAD/EDG-for-VisNet/visnet_for_EDG/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"65759d84ca6eef27","mcp_get_code":{"code_sha256":"65759d84ca6eef27"}},{"arxiv_id":"aaai_35067","paper":null,"title":"arXiv:aaai_35067","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"unicef/giga-global-school-mapping","path":"utils/model_utils.py","file_url":"https://github.com/unicef/giga-global-school-mapping/blob/HEAD/utils/model_utils.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":"527fd02693c69301","mcp_get_code":{"code_sha256":"527fd02693c69301"}}]}