{"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/listdir","entry":"listdir","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":12,"n_papers_ran":3,"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":10,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":12,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":1,"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":"2307.14565","paper":"/paper/auto-tables-synthesizing-multi-step","title":"Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples","date":"2023-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lipengcs/auto-tables-benchmark","path":"evaluate.py","file_url":"https://github.com/lipengcs/auto-tables-benchmark/blob/HEAD/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cca0d3226c262115","mcp_get_code":{"code_sha256":"cca0d3226c262115"}},{"arxiv_id":"2306.17723","paper":"/paper/flipnerf-flipped-reflection-rays-for-few-shot","title":"FlipNeRF: Flipped Reflection Rays for Few-shot Novel View Synthesis","date":"2023-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shawn615/FlipNeRF","path":"internal/utils.py","file_url":"https://github.com/shawn615/FlipNeRF/blob/HEAD/internal/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0bc10c560cd1a9d0","mcp_get_code":{"code_sha256":"0bc10c560cd1a9d0"}},{"arxiv_id":"2302.08788","paper":"/paper/mixnerf-modeling-a-ray-with-mixture-density","title":"MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse Inputs","date":"2023-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shawn615/MixNeRF","path":"internal/utils.py","file_url":"https://github.com/shawn615/MixNeRF/blob/HEAD/internal/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0bc10c560cd1a9d0","mcp_get_code":{"code_sha256":"0bc10c560cd1a9d0"}},{"arxiv_id":"2206.07802","paper":"/paper/what-makes-domain-generalization-hard","title":"Improving generalization by mimicking the human visual diet","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"spandan-madan/human_visual_diet","path":"rendering/add_materials_to_deformed_meshes.py","file_url":"https://github.com/spandan-madan/human_visual_diet/blob/HEAD/rendering/add_materials_to_deformed_meshes.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c8e896d2aa173c7e","mcp_get_code":{"code_sha256":"c8e896d2aa173c7e"}},{"arxiv_id":"2110.04176","paper":"/paper/lightweight-convolutional-neural-networks-by","title":"PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions","date":"2021-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ispamm/hi2i","path":"core/data_loader.py","file_url":"https://github.com/ispamm/hi2i/blob/HEAD/core/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d07d6906ac8b165a","mcp_get_code":{"code_sha256":"d07d6906ac8b165a"}},{"arxiv_id":"2107.00644","paper":"/paper/stabilizing-deep-q-learning-with-convnets-and","title":"Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation","date":"2021-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nicklashansen/svea-vit","path":"src/utils.py","file_url":"https://github.com/nicklashansen/svea-vit/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4cd7bffb72ec1909","mcp_get_code":{"code_sha256":"4cd7bffb72ec1909"}},{"arxiv_id":"2103.13415","paper":"/paper/mip-nerf-a-multiscale-representation-for-anti","title":"Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields","date":"2021-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/mipnerf","path":"internal/utils.py","file_url":"https://github.com/google/mipnerf/blob/HEAD/internal/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":"0bc10c560cd1a9d0","mcp_get_code":{"code_sha256":"0bc10c560cd1a9d0"}},{"arxiv_id":"1912.05663","paper":"/paper/measuring-the-reliability-of-reinforcement-1","title":"Measuring the Reliability of Reinforcement Learning Algorithms","date":"2019-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/rl-reliability-metrics","path":"rl_reliability_metrics/analysis/io_utils_oss.py","file_url":"https://github.com/google-research/rl-reliability-metrics/blob/HEAD/rl_reliability_metrics/analysis/io_utils_oss.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":"48350999d3b51fb7","mcp_get_code":{"code_sha256":"48350999d3b51fb7"}},{"arxiv_id":"1911.07420","paper":"/paper/a-graph-autoencoder-approach-to-causal","title":"A Graph Autoencoder Approach to Causal Structure Learning","date":"2019-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ThinkNaive/GAE-PSP","path":"tools/tune_graph_threshold.py","file_url":"https://github.com/ThinkNaive/GAE-PSP/blob/HEAD/tools/tune_graph_threshold.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1948e6e2d16c626b","mcp_get_code":{"code_sha256":"1948e6e2d16c626b"}},{"arxiv_id":"1904.01189","paper":"/paper/semantics-guided-neural-networks-for","title":"Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"urw7rs/torch_skeleton","path":"torch_skeleton/utils.py","file_url":"https://github.com/urw7rs/torch_skeleton/blob/HEAD/torch_skeleton/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c4b7f9203fdb9f3d","mcp_get_code":{"code_sha256":"c4b7f9203fdb9f3d"}},{"arxiv_id":"1711.01558","paper":"/paper/wasserstein-auto-encoders","title":"Wasserstein Auto-Encoders","date":"2017-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tolstikhin/wae","path":"utils.py","file_url":"https://github.com/tolstikhin/wae/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"23aa6f5ab0423a4d","mcp_get_code":{"code_sha256":"23aa6f5ab0423a4d"}},{"arxiv_id":"Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper","paper":null,"title":"arXiv:Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KU-CVLAB/LANIT","path":"core/data_loader.py","file_url":"https://github.com/KU-CVLAB/LANIT/blob/HEAD/core/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c4bec6fa0023c750","mcp_get_code":{"code_sha256":"c4bec6fa0023c750"}}]}