{"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/sce-loss","entry":"sce_loss","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":6,"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":9,"n_samples_ran":5,"n_samples_fingerprinted":4,"n_places":16,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":4,"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":"2609.00696","paper":"/paper/arxiv-2609-00696","title":"MUGEN: Generating Unlearnable Graph Examples for Multiple Learning Tasks","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"ZiyanLiu16/MUGEN","path":"mugen/models/loss_func.py","file_url":"https://github.com/ZiyanLiu16/MUGEN/blob/HEAD/mugen/models/loss_func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f9bb5eb98ec4887","mcp_get_code":{"code_sha256":"6f9bb5eb98ec4887"}},{"arxiv_id":"2605.07812","paper":"/paper/arxiv-2605-07812","title":"GRASP -Graph-Based Anomaly Detection Through Self-Supervised Classification","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"ubc-provenance/PIDSMaker","path":"pidsmaker/losses.py","file_url":"https://github.com/ubc-provenance/PIDSMaker/blob/HEAD/pidsmaker/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"300349a15550aacf","mcp_get_code":{"code_sha256":"300349a15550aacf"}},{"arxiv_id":"2601.13331","paper":"/paper/arxiv-2601-13331","title":"MultiST: A Cross-Attention-Based Multimodal Model for Spatial Transcriptomics","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"LabJunBMI/MultiST","path":"MultiST/MultiST/MultiST_model.py","file_url":"https://github.com/LabJunBMI/MultiST/blob/HEAD/MultiST/MultiST/MultiST_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":"89840e9ddc948130","mcp_get_code":{"code_sha256":"89840e9ddc948130"}},{"arxiv_id":"2410.13798","paper":"/paper/learning-graph-quantized-tokenizers-for","title":"Learning Graph Quantized Tokenizers","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"limei0307/GQT","path":"gmae2_models/loss_func.py","file_url":"https://github.com/limei0307/GQT/blob/HEAD/gmae2_models/loss_func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f9bb5eb98ec4887","mcp_get_code":{"code_sha256":"6f9bb5eb98ec4887"}},{"arxiv_id":"2407.16863","paper":"/paper/balanced-multi-relational-graph-clustering","title":"Balanced Multi-Relational Graph Clustering","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zxlearningdeep/bmgc","path":"BMGC/module/loss_fun.py","file_url":"https://github.com/zxlearningdeep/bmgc/blob/HEAD/BMGC/module/loss_fun.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6188cda4ac7f17da","mcp_get_code":{"code_sha256":"6188cda4ac7f17da"}},{"arxiv_id":"2404.15806","paper":"/paper/where-to-mask-structure-guided-masking-for","title":"Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders","date":"2024-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiuChuang0059/StructMAE","path":"StructMAE-L/chem/pretraining.py","file_url":"https://github.com/LiuChuang0059/StructMAE/blob/HEAD/StructMAE-L/chem/pretraining.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80efc9c262d235a6","mcp_get_code":{"code_sha256":"80efc9c262d235a6"}},{"arxiv_id":"2402.13630","paper":"/paper/unigraph-learning-a-cross-domain-graph","title":"UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yf-he/UniGraph","path":"utils/loss_func.py","file_url":"https://github.com/yf-he/UniGraph/blob/HEAD/utils/loss_func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f9bb5eb98ec4887","mcp_get_code":{"code_sha256":"6f9bb5eb98ec4887"}},{"arxiv_id":"2402.07225","paper":"/paper/rethinking-graph-masked-autoencoders-through","title":"Rethinking Graph Masked Autoencoders through Alignment and Uniformity","date":"2024-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AzureLeon1/AUG-MAE","path":"augmae/models/loss_func.py","file_url":"https://github.com/AzureLeon1/AUG-MAE/blob/HEAD/augmae/models/loss_func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f9bb5eb98ec4887","mcp_get_code":{"code_sha256":"6f9bb5eb98ec4887"}},{"arxiv_id":"2310.00800","paper":null,"title":"arXiv:2310.00800","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":null,"repo":"jumxglhf/ParetoGNN","path":"src/model.py","file_url":"https://github.com/jumxglhf/ParetoGNN/blob/HEAD/src/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"6f9bb5eb98ec4887","mcp_get_code":{"code_sha256":"6f9bb5eb98ec4887"}},{"arxiv_id":"2309.15123","paper":"/paper/uncovering-neural-scaling-laws-in-molecular-1","title":"Uncovering Neural Scaling Laws in Molecular Representation Learning","date":"2023-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"data-reindeer/nsl_mrl","path":"main_pretrain.py","file_url":"https://github.com/data-reindeer/nsl_mrl/blob/HEAD/main_pretrain.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80efc9c262d235a6","mcp_get_code":{"code_sha256":"80efc9c262d235a6"}},{"arxiv_id":"2309.04589","paper":"/paper/motif-aware-attribute-masking-for-molecular","title":"Motif-aware Attribute Masking for Molecular Graph Pre-training","date":"2023-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"einae-nd/moama-dev","path":"pretrain.py","file_url":"https://github.com/einae-nd/moama-dev/blob/HEAD/pretrain.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c8e38ae70c7e3a31","mcp_get_code":{"code_sha256":"c8e38ae70c7e3a31"}},{"arxiv_id":"2309.04589","paper":"/paper/motif-aware-attribute-masking-for-molecular","title":"Motif-aware Attribute Masking for Molecular Graph Pre-training","date":"2023-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"einae-nd/moama-dev","path":"gnn_model.py","file_url":"https://github.com/einae-nd/moama-dev/blob/HEAD/gnn_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f9bb5eb98ec4887","mcp_get_code":{"code_sha256":"6f9bb5eb98ec4887"}},{"arxiv_id":"2306.10649","paper":"/paper/companykg-a-large-scale-heterogeneous-graph","title":"CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification","date":"2023-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eqtpartners/companykg","path":"benchmarks/src/ckg_benchmarks/graphmae/model.py","file_url":"https://github.com/eqtpartners/companykg/blob/HEAD/benchmarks/src/ckg_benchmarks/graphmae/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ed7c63effd809589","mcp_get_code":{"code_sha256":"ed7c63effd809589"}},{"arxiv_id":"2304.04779","paper":"/paper/graphmae2-a-decoding-enhanced-masked-self","title":"GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner","date":"2023-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thudm/graphmae2","path":"models/loss_func.py","file_url":"https://github.com/thudm/graphmae2/blob/HEAD/models/loss_func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f9bb5eb98ec4887","mcp_get_code":{"code_sha256":"6f9bb5eb98ec4887"}},{"arxiv_id":"2212.00309","paper":"/paper/differentially-private-adaptive-optimization","title":"Differentially Private Adaptive Optimization with Delayed Preconditioners","date":"2022-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kenziyuliu/dp2","path":"trainers/dp2_adagrad.py","file_url":"https://github.com/kenziyuliu/dp2/blob/HEAD/trainers/dp2_adagrad.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":"1d3f9fc33b06bb7f","mcp_get_code":{"code_sha256":"1d3f9fc33b06bb7f"}},{"arxiv_id":"2204.07596","paper":"/paper/perfectly-balanced-improving-transfer-and","title":"Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning","date":"2022-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HazyResearch/thanos-code","path":"unagi/tasks/loss_modules.py","file_url":"https://github.com/HazyResearch/thanos-code/blob/HEAD/unagi/tasks/loss_modules.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":"9c5cbb4a3040b938","mcp_get_code":{"code_sha256":"9c5cbb4a3040b938"}}]}