{"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/concat-elu","entry":"concat_elu","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":8,"n_papers_ran":4,"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":2,"n_places":8,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"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":"2309.02190","paper":"/paper/exchanging-based-multimodal-fusion-with","title":"Exchanging-based Multimodal Fusion with Transformer","date":"2023-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"recklessronan/muse","path":"code/models.py","file_url":"https://github.com/recklessronan/muse/blob/HEAD/code/models.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c2bd9d10c497055b","mcp_get_code":{"code_sha256":"c2bd9d10c497055b"}},{"arxiv_id":"2110.08787","paper":"/paper/pixelpyramids-exact-inference-models-from-1","title":"PixelPyramids: Exact Inference Models from Lossless Image Pyramids","date":"2021-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"1bc8478089c8a7c9","mcp_get_code":{"code_sha256":"1bc8478089c8a7c9"}},{"arxiv_id":"1906.12320","paper":"/paper/pointflow-3d-point-cloud-generation-with","title":"PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows","date":"2019-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visinf/s2-flow","path":"util/array_util.py","file_url":"https://github.com/visinf/s2-flow/blob/HEAD/util/array_util.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":"d0ea4df0e7ec8ba6","mcp_get_code":{"code_sha256":"d0ea4df0e7ec8ba6"}},{"arxiv_id":"1905.08500","paper":"/paper/compression-with-flows-via-local-bits-back","title":"Compression with Flows via Local Bits-Back Coding","date":"2019-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hojonathanho/localbitsback","path":"compression/coupling.py","file_url":"https://github.com/hojonathanho/localbitsback/blob/HEAD/compression/coupling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9953b28b1527a27e","mcp_get_code":{"code_sha256":"9953b28b1527a27e"}},{"arxiv_id":"1807.10589","paper":"/paper/diverse-feature-visualizations-reveal","title":"Diverse feature visualizations reveal invariances in early layers of deep neural networks","date":"2018-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sacadena/diverse_feature_vis","path":"pixel-cnn/pixel_cnn_pp/nn.py","file_url":"https://github.com/sacadena/diverse_feature_vis/blob/HEAD/pixel-cnn/pixel_cnn_pp/nn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"919f423798c6a32a","mcp_get_code":{"code_sha256":"919f423798c6a32a"}},{"arxiv_id":"1712.09763","paper":"/paper/pixelsnail-an-improved-autoregressive","title":"PixelSNAIL: An Improved Autoregressive Generative Model","date":"2017-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kamenbliznashki/pixel_models","path":"pixelsnail.py","file_url":"https://github.com/kamenbliznashki/pixel_models/blob/HEAD/pixelsnail.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":"1bc8478089c8a7c9","mcp_get_code":{"code_sha256":"1bc8478089c8a7c9"}},{"arxiv_id":"1702.08658","paper":"/paper/towards-deeper-understanding-of-variational","title":"Towards Deeper Understanding of Variational Autoencoding Models","date":"2017-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShengjiaZhao/Generalized-PixelVAE","path":"pixel_cnn_pp/nn.py","file_url":"https://github.com/ShengjiaZhao/Generalized-PixelVAE/blob/HEAD/pixel_cnn_pp/nn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c2617c0e25b5f26a","mcp_get_code":{"code_sha256":"c2617c0e25b5f26a"}},{"arxiv_id":"1701.05517","paper":"/paper/pixelcnn-improving-the-pixelcnn-with","title":"PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications","date":"2017-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"1bc8478089c8a7c9","mcp_get_code":{"code_sha256":"1bc8478089c8a7c9"}}]}