{"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/get-optim","entry":"get_optim","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":13,"n_samples_ran":6,"n_samples_fingerprinted":0,"n_places":15,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":5,"unverified":7},"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":"2412.08128","paper":"/paper/why-does-dropping-edges-usually-outperform","title":"Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?","date":"2024-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hyzhang98/epagcl","path":"Utils.py","file_url":"https://github.com/hyzhang98/epagcl/blob/HEAD/Utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e94915c3b426976","mcp_get_code":{"code_sha256":"7e94915c3b426976"}},{"arxiv_id":"2410.06262","paper":"/paper/symdiff-equivariant-diffusion-via-stochastic","title":"SymDiff: Equivariant Diffusion via Stochastic Symmetrisation","date":"2024-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leozhangML/SymDiff","path":"qm9/models.py","file_url":"https://github.com/leozhangML/SymDiff/blob/HEAD/qm9/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"85c8c5cc8774ca97","mcp_get_code":{"code_sha256":"85c8c5cc8774ca97"}},{"arxiv_id":"2407.16193","paper":"/paper/cloudfixer-test-time-adaptation-for-3d-point","title":"CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformation","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shimazing/CloudFixer","path":"diffusion/build_model.py","file_url":"https://github.com/shimazing/CloudFixer/blob/HEAD/diffusion/build_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f00fffed990205f4","mcp_get_code":{"code_sha256":"f00fffed990205f4"}},{"arxiv_id":"2401.10227","paper":"/paper/a-simple-latent-diffusion-approach-for","title":"A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting","date":"2024-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"segments-ai/latent-diffusion-segmentation","path":"ldmseg/trainers/optim.py","file_url":"https://github.com/segments-ai/latent-diffusion-segmentation/blob/HEAD/ldmseg/trainers/optim.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"285d6b71a60bea8d","mcp_get_code":{"code_sha256":"285d6b71a60bea8d"}},{"arxiv_id":"2311.18495","paper":"/paper/improving-adversarial-transferability-via-2","title":"Improving Adversarial Transferability via Model Alignment","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"averyma/model-alignment","path":"src/utils_general.py","file_url":"https://github.com/averyma/model-alignment/blob/HEAD/src/utils_general.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"246863313c06da67","mcp_get_code":{"code_sha256":"246863313c06da67"}},{"arxiv_id":"2308.06703","paper":"/paper/understanding-the-robustness-difference","title":"Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods","date":"2023-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"averyma/opt-robust","path":"src/utils_general.py","file_url":"https://github.com/averyma/opt-robust/blob/HEAD/src/utils_general.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"634dbd17789eb3c5","mcp_get_code":{"code_sha256":"634dbd17789eb3c5"}},{"arxiv_id":"2305.14836","paper":"/paper/nuscenes-qa-a-multi-modal-visual-question","title":"NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario","date":"2023-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qiantianwen/nuscenes-qa","path":"src/utils/optim.py","file_url":"https://github.com/qiantianwen/nuscenes-qa/blob/HEAD/src/utils/optim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9e822b9d34a49db0","mcp_get_code":{"code_sha256":"9e822b9d34a49db0"}},{"arxiv_id":"2303.01903","paper":"/paper/prompting-large-language-models-with-answer","title":"Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering","date":"2023-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"milvlg/prophet","path":"prophet/stage1/utils/optim.py","file_url":"https://github.com/milvlg/prophet/blob/HEAD/prophet/stage1/utils/optim.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":"4120d55fb48dd6b0","mcp_get_code":{"code_sha256":"4120d55fb48dd6b0"}},{"arxiv_id":"2202.05008","paper":"/paper/evojax-hardware-accelerated-neuroevolution","title":"EvoJAX: Hardware-Accelerated Neuroevolution","date":"2022-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/evojax","path":"evojax/algo/ars_native.py","file_url":"https://github.com/google/evojax/blob/HEAD/evojax/algo/ars_native.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":"d3bae217f149bd47","mcp_get_code":{"code_sha256":"d3bae217f149bd47"}},{"arxiv_id":"2107.09106","paper":"/paper/separating-skills-and-concepts-for-novel-1","title":"Separating Skills and Concepts for Novel Visual Question Answering","date":"2021-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SpencerWhitehead/novelvqa","path":"core/model/optim.py","file_url":"https://github.com/SpencerWhitehead/novelvqa/blob/HEAD/core/model/optim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b6bf5a95f3801883","mcp_get_code":{"code_sha256":"b6bf5a95f3801883"}},{"arxiv_id":"2002.07971","paper":"/paper/gradient-boosting-neural-networks-grownet","title":"Gradient Boosting Neural Networks: GrowNet","date":"2020-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sbadirli/GrowNet","path":"Classification/main_cls_cv.py","file_url":"https://github.com/sbadirli/GrowNet/blob/HEAD/Classification/main_cls_cv.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dcaab65615b91b2d","mcp_get_code":{"code_sha256":"dcaab65615b91b2d"}},{"arxiv_id":"1906.10770","paper":"/paper/deep-modular-co-attention-networks-for-visual-1","title":"Deep Modular Co-Attention Networks for Visual Question Answering","date":"2019-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MILVLG/mcan-vqa","path":"core/model/optim.py","file_url":"https://github.com/MILVLG/mcan-vqa/blob/HEAD/core/model/optim.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":"b6bf5a95f3801883","mcp_get_code":{"code_sha256":"b6bf5a95f3801883"}},{"arxiv_id":"aaai_28607","paper":null,"title":"arXiv:aaai_28607","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"GuangmingZhu/SketchESC","path":"inversion/main_inversion.py","file_url":"https://github.com/GuangmingZhu/SketchESC/blob/HEAD/inversion/main_inversion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89169ce9effdd614","mcp_get_code":{"code_sha256":"89169ce9effdd614"}},{"arxiv_id":"aaai_28253","paper":null,"title":"arXiv:aaai_28253","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"qiantianwen/NuScenes-QA","path":"src/utils/optim.py","file_url":"https://github.com/qiantianwen/NuScenes-QA/blob/HEAD/src/utils/optim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9e822b9d34a49db0","mcp_get_code":{"code_sha256":"9e822b9d34a49db0"}},{"arxiv_id":"2023.findings-acl.810","paper":null,"title":"arXiv:2023.findings-acl.810","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yoyo-yun/DG_RRR","path":"models/get_optim.py","file_url":"https://github.com/yoyo-yun/DG_RRR/blob/HEAD/models/get_optim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a034475a3896a941","mcp_get_code":{"code_sha256":"a034475a3896a941"}}]}