{"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-cmap","entry":"get_cmap","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":11,"n_papers_ran":0,"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":0,"n_samples_fingerprinted":0,"n_places":11,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":9},"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":"2504.07092","paper":"/paper/are-we-done-with-object-centric-learning","title":"Are We Done with Object-Centric Learning?","date":"2025-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexanderrubinstein/occam","path":"occam/submodules/dino_ft_wrapper.py","file_url":"https://github.com/alexanderrubinstein/occam/blob/HEAD/occam/submodules/dino_ft_wrapper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eed8e2950817c12c","mcp_get_code":{"code_sha256":"eed8e2950817c12c"}},{"arxiv_id":"2303.13446","paper":"/paper/on-the-utility-of-koopman-operator-theory-in","title":"On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills","date":null,"month_inferred_from_arxiv_id":"2023-03","title_source":"archive","repo":"gt-star-lab/kodex","path":"Door/Koopman_training.py","file_url":"https://github.com/gt-star-lab/kodex/blob/HEAD/Door/Koopman_training.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"56fccc8ce3053ac3","mcp_get_code":{"code_sha256":"56fccc8ce3053ac3"}},{"arxiv_id":"2303.04679","paper":"/paper/flow-reconstruction-by-multiresolution","title":"Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation","date":null,"month_inferred_from_arxiv_id":"2023-03","title_source":"archive","repo":"cselab/odil","path":"src/odil/plot.py","file_url":"https://github.com/cselab/odil/blob/HEAD/src/odil/plot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a8278e57206d1bfa","mcp_get_code":{"code_sha256":"a8278e57206d1bfa"}},{"arxiv_id":"2301.11845","paper":"/paper/learning-the-effects-of-physical-actions-in-a","title":"Learning the Effects of Physical Actions in a Multi-modal Environment","date":"2023-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gautierdag/piglet-vis","path":"pigletvis/models/analysis.py","file_url":"https://github.com/gautierdag/piglet-vis/blob/HEAD/pigletvis/models/analysis.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf535327d6a8febe","mcp_get_code":{"code_sha256":"bf535327d6a8febe"}},{"arxiv_id":"2210.14451","paper":"/paper/discovering-design-concepts-for-cad-sketches","title":"Discovering Design Concepts for CAD Sketches","date":"2022-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yyuezhi/sketchconcept","path":"plot_interactive_visualize.py","file_url":"https://github.com/yyuezhi/sketchconcept/blob/HEAD/plot_interactive_visualize.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ed37c72ac1938be8","mcp_get_code":{"code_sha256":"ed37c72ac1938be8"}},{"arxiv_id":"2209.12890","paper":"/paper/it-takes-two-learning-to-plan-for-human-robot","title":"It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying","date":"2022-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eleyng/cooperative_planner","path":"utils/vis.py","file_url":"https://github.com/eleyng/cooperative_planner/blob/HEAD/utils/vis.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"96f99dd13b447c11","mcp_get_code":{"code_sha256":"96f99dd13b447c11"}},{"arxiv_id":"2203.12573","paper":"/paper/serialtrack-scale-and-rotation-invariant","title":"SerialTrack: ScalE and Rotation Invariant Augmented Lagrangian Particle Tracking","date":null,"month_inferred_from_arxiv_id":"2022-03","title_source":"archive","repo":"FranckLab/SerialTrack","path":"SerialTrack2D/function/pyplotCMap2txt.py","file_url":"https://github.com/FranckLab/SerialTrack/blob/HEAD/SerialTrack2D/function/pyplotCMap2txt.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7353408a023bbf92","mcp_get_code":{"code_sha256":"7353408a023bbf92"}},{"arxiv_id":"2203.09978","paper":"/paper/woods-benchmarks-for-out-of-distribution","title":"WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series","date":"2022-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jc-audet/WOODS","path":"woods/utils.py","file_url":"https://github.com/jc-audet/WOODS/blob/HEAD/woods/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"56fccc8ce3053ac3","mcp_get_code":{"code_sha256":"56fccc8ce3053ac3"}},{"arxiv_id":"2110.07586","paper":"/paper/can-explanations-be-useful-for-calibrating","title":"Can Explanations Be Useful for Calibrating Black Box Models?","date":"2021-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangalan123/amortized-interpretability","path":"draw_feature_selection.py","file_url":"https://github.com/yangalan123/amortized-interpretability/blob/HEAD/draw_feature_selection.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"96f99dd13b447c11","mcp_get_code":{"code_sha256":"96f99dd13b447c11"}},{"arxiv_id":"2107.00848","paper":"/paper/systematic-evaluation-of-causal-discovery-in-1","title":"Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning","date":"2021-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dido1998/CausalMBRL","path":"cswm/utils.py","file_url":"https://github.com/dido1998/CausalMBRL/blob/HEAD/cswm/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3ad1b4670dbfe46d","mcp_get_code":{"code_sha256":"3ad1b4670dbfe46d"}},{"arxiv_id":"2106.07643","paper":"/paper/unsupervised-learning-of-visual-3d-keypoints","title":"Unsupervised Learning of Visual 3D Keypoints for Control","date":"2021-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"buoyancy99/unsup-3d-keypoints","path":"algorithms/common/models/keypoint_net.py","file_url":"https://github.com/buoyancy99/unsup-3d-keypoints/blob/HEAD/algorithms/common/models/keypoint_net.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"174c85c073dd49b0","mcp_get_code":{"code_sha256":"174c85c073dd49b0"}}]}