{"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":"/census/geglu-2","entry":"geglu","source":"Syntology differential census (groundwork/55, run v2_2026-09-22), per sample; not an archive number","census_date":"2026-09-22","battery_sha256":["b986f7e04d794a0d88ad4c5f32cf63ec3590b5deff0192150737bc5f1c0b4677"],"runner_sha256":["5a452d0e7c0da5b80771d1be2afe3572e253568cf5c5d59a0d08ebd663d00808"],"bucket_key":"positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name","bucket_fields":["rank","kind","dtype"],"claim":"Implementations sharing this entry name were each run on one shared input fixed by the positional (rank, kind, dtype) of their array arguments (the bucket). A cluster is the set whose recorded output digest (sha256 of the output rounded to 6 decimals) is identical. Identical values to six decimals on the shared input are agreement on those inputs, not a statement about the whole domain and not a substitution claim.","n_implementations_compared":3,"n_papers":5,"n_buckets":1,"n_distinct_outputs":1,"n_class_bearing":0,"not_compared":{"not_run_on_shared_input":{"n":0,"by_error":{}},"no_array_argument_ran_on_own_fixture_arguments_only":{"n":0,"n_papers":0,"recorded_shared_digest_equals_own_fixture_digest":0},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/geglu-2","buckets":[{"bucket":[[2,"float","float32"]],"n_implementations_compared":3,"n_papers":5,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"2fde4292a2c28363","size":3,"n_papers":5,"shape":[4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":3,"torch_reference_conventions_with_this_digest":[],"values":[-0.29832589626312256,0.23928076028823853,-0.08005796372890472,-0.2836473882198334,-0.05785972625017166,2.2047152519226074,0.0620269812643528,-0.6121837496757507,-0.0007365558412857354,0.22773750126361847,-0.06859372556209564,-0.33457183837890625,0.10741380602121353,0.496163547039032,-0.1162596344947815,-0.16697940230369568],"values_recorded":16,"members":[{"code_sha256_prefix":"49f8fe0655f00ee5","path":"tabsyn/model.py","papers":["2405.20690","2102.08921","2601.22816"],"paper_pages":[{"arxiv_id":"2405.20690","page":"/paper/unleashing-the-potential-of-diffusion-models"},{"arxiv_id":"2102.08921","page":"/paper/how-faithful-is-your-synthetic-data-sample"},{"arxiv_id":"2601.22816","page":"/paper/arxiv-2601-22816"}],"arg_sig_recorded":[["x",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"647ed25965ec25b1","path":"models/predictive_models.py","papers":["2309.16220"],"paper_pages":[{"arxiv_id":"2309.16220","page":"/paper/unmasking-the-chameleons-a-benchmark-for-out"}],"arg_sig_recorded":[["x",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"d7bace13fb369137","path":"models/t2g.py","papers":["2403.01570"],"paper_pages":[{"arxiv_id":"2403.01570","page":"/paper/serval-synergy-learning-between-vertical"}],"arg_sig_recorded":[["x",2,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]}]}