{"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/func","entry":"func","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":5,"n_papers":5,"n_buckets":4,"n_distinct_outputs":5,"n_class_bearing":0,"not_compared":{"not_run_on_shared_input":{"n":2,"by_error":{"RuntimeError":2}},"no_array_argument_ran_on_own_fixture_arguments_only":{"n":2,"n_papers":2,"recorded_shared_digest_equals_own_fixture_digest":2},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/func","buckets":[{"bucket":[[1,"float","float64"]],"n_implementations_compared":2,"n_papers":2,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"018087ba8e94a9ea","size":1,"n_papers":1,"shape":[8],"dtype":"float64","type":"ndarray","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[-0.7031027691632896,0.7183483387243528,0.14936810010540108,-0.9744291265576945,-0.24925280634576397,0.4542751468464892,1.1021623836997265,-0.25319359909830563],"values_recorded":8,"members":[{"code_sha256_prefix":"8da9e8b61aaa0969","path":"experiments/demo_1d_regression.py","papers":["1912.13440"],"paper_pages":[{"arxiv_id":"1912.13440","page":"/paper/approximate-inference-for-fully-bayesian"}],"arg_sig_recorded":[["x",1,"float","float64"]],"scalar_args":{},"class_bearing":false}]},{"output_sha":"2b88bbb320f4caa7","size":1,"n_papers":1,"shape":[8],"dtype":"float64","type":"ndarray","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[2.5160549321034256,2.986578862950825,2.583895952583333,2.660131480290342,2.982680422251067,2.9984593734944496,1.3180659158799979,2.9824266405329665],"values_recorded":8,"members":[{"code_sha256_prefix":"f5555e4360d6910c","path":"train_3d_op.py","papers":["2201.04021"],"paper_pages":[{"arxiv_id":"2201.04021","page":"/paper/optimization-planning-for-3d-convnets-1"}],"arg_sig_recorded":[["x",1,"float","float64"]],"scalar_args":{"a":"2.0","b":"0.5","c":"1.0","d":"0.3","e":"5.0"},"class_bearing":false}]}]},{"bucket":[[0,"float","float32"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"afdf3508e713c611","size":1,"n_papers":1,"shape":[1],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.7830805778503418],"values_recorded":1,"members":[{"code_sha256_prefix":"c6f677b13c0cb70f","path":"dqn_model_r.py","papers":["2012.08950"],"paper_pages":[{"arxiv_id":"2012.08950","page":"/paper/deep-reinforcement-learning-of-graph-matching"}],"arg_sig_recorded":[["x",0,"float","float32"]],"scalar_args":{"params":"(tensor(2.), tensor(3.), tensor(1.))"},"class_bearing":false}]}]},{"bucket":[[1,"float","float64"],[1,"float","float64"],[1,"float","float64"],[1,"float","float64"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"7602a99aa9499722","size":1,"n_papers":1,"shape":[8],"dtype":"float64","type":"ndarray","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[-0.9038889997942827,0.15713145715131374,0.8976119866651816,-0.7621955414462447,-0.17625556327648054,0.053031720890262375,-1.628440281615731,-0.17753333139257513],"values_recorded":8,"members":[{"code_sha256_prefix":"f34d11007be3a537","path":"spec_generation.py","papers":["2201.08967"],"paper_pages":[{"arxiv_id":"2201.08967","page":"/paper/a-robust-hot-subdwarfs-identification-method"}],"arg_sig_recorded":[["r",1,"float","float64"],["a",1,"float","float64"],["x",1,"float","float64"],["b",1,"float","float64"]],"scalar_args":{},"class_bearing":false}]}]},{"bucket":[[1,"float","float64"],[1,"float","float64"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"b03d35c041830078","size":1,"n_papers":1,"shape":[8],"dtype":"float64","type":"ndarray","finite":false,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[NaN,-0.2214397038668874,-0.011659444302274915,NaN,NaN,-0.13186014635290788,NaN,NaN],"values_recorded":8,"members":[{"code_sha256_prefix":"073babdc8a31a49d","path":"ID_estimate/ID_graph_largedata.py","papers":["1803.09672"],"paper_pages":[{"arxiv_id":"1803.09672","page":"/paper/on-the-intrinsic-dimensionality-of-face"}],"arg_sig_recorded":[["x",1,"float","float64"],["a",1,"float","float64"]],"scalar_args":{"b":"0.0","c":"0.0"},"class_bearing":false}]}]}]}