{"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/eye-like","entry":"eye_like","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":5,"n_papers_ran":2,"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":2,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":1,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"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":"2406.05027","paper":"/paper/optimizing-automatic-differentiation-with","title":"Optimizing Automatic Differentiation with Deep Reinforcement Learning","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jamielohoff/graphax","path":"src/graphax/sparse/utils.py","file_url":"https://github.com/jamielohoff/graphax/blob/HEAD/src/graphax/sparse/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f493bdb09a28f07c","mcp_get_code":{"code_sha256":"f493bdb09a28f07c"}},{"arxiv_id":"2306.02342","paper":"/paper/deep-optimal-transport-a-practical-algorithm-1","title":"Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration","date":"2023-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"theoad/dot-dmax","path":"dmax/latent_w2.py","file_url":"https://github.com/theoad/dot-dmax/blob/HEAD/dmax/latent_w2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"96c56dcdbd0d5802","mcp_get_code":{"code_sha256":"96c56dcdbd0d5802"}},{"arxiv_id":"2304.10628","paper":"/paper/hm-vit-hetero-modal-vehicle-to-vehicle","title":"HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformer","date":"2023-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XHwind/HM-ViT","path":"opencood/models/sub_modules/hetero_fusion.py","file_url":"https://github.com/XHwind/HM-ViT/blob/HEAD/opencood/models/sub_modules/hetero_fusion.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2ddac2e2ccb49f1e","mcp_get_code":{"code_sha256":"2ddac2e2ccb49f1e"}},{"arxiv_id":"2111.13738","paper":"/paper/the-implicit-values-of-a-good-hand-shake","title":"The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement","date":"2021-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-computational-imaging/hndr","path":"model.py","file_url":"https://github.com/princeton-computational-imaging/hndr/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"40e8c5b4da963fbc","mcp_get_code":{"code_sha256":"40e8c5b4da963fbc"}},{"arxiv_id":"2111.13738","paper":"/paper/the-implicit-values-of-a-good-hand-shake","title":"The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement","date":"2021-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-computational-imaging/hndr","path":"utils/dataloader.py","file_url":"https://github.com/princeton-computational-imaging/hndr/blob/HEAD/utils/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"74ada9e2cd143d24","mcp_get_code":{"code_sha256":"74ada9e2cd143d24"}},{"arxiv_id":"2005.05220","paper":"/paper/iunets-fully-invertible-u-nets-with-learnable","title":"iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling","date":"2020-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cetmann/iunets","path":"iunets/utils.py","file_url":"https://github.com/cetmann/iunets/blob/HEAD/iunets/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"580b5bba856a169e","mcp_get_code":{"code_sha256":"580b5bba856a169e"}}]}