{"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/mse","entry":"mse","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":6,"n_buckets":3,"n_distinct_outputs":4,"n_class_bearing":0,"not_compared":{"not_run_on_shared_input":{"n":1,"by_error":{"RuntimeError":1}},"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/mse","buckets":[{"bucket":[[2,"float","float32"],[2,"float","float32"]],"n_implementations_compared":3,"n_papers":4,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"af5570f5a1810b7a","size":2,"n_papers":2,"shape":[],"dtype":"float32","type":"float32","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":2,"torch_reference_conventions_with_this_digest":[],"values":[0.0],"values_recorded":1,"members":[{"code_sha256_prefix":"49b3c2389c985e00","path":"completion/icnn_ebundle.py","papers":["1609.07152"],"paper_pages":[{"arxiv_id":"1609.07152","page":"/paper/input-convex-neural-networks"}],"arg_sig_recorded":[["y",2,"float","float32"],["trueY",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"79bd575f8de311d9","path":"YearMSD/SDE_regression.py","papers":["2008.10546"],"paper_pages":[{"arxiv_id":"2008.10546","page":"/paper/sde-net-equipping-deep-neural-networks-with"}],"arg_sig_recorded":[["y",2,"float","float32"],["mean",2,"float","float32"]],"scalar_args":{},"class_bearing":false}]},{"output_sha":"5341e6b2646979a7","size":1,"n_papers":2,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"values_recorded":32,"members":[{"code_sha256_prefix":"8d8f088c9186b5d4","path":"src/algorithm/tdmpc.py","papers":["2310.16828","2404.03037"],"paper_pages":[{"arxiv_id":"2310.16828","page":"/paper/td-mpc2-scalable-robust-world-models-for"},{"arxiv_id":"2404.03037","page":"/paper/model-based-reinforcement-learning-for-7"}],"arg_sig_recorded":[["pred",2,"float","float32"],["target",2,"float","float32"]],"scalar_args":{"reduce":"False"},"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":"af5570f5a1810b7a","size":1,"n_papers":1,"shape":[],"dtype":"float64","type":"float","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.0],"values_recorded":1,"members":[{"code_sha256_prefix":"17b642db91f20cc2","path":"lid/evaluation/lid_evaluation.py","papers":["2406.03537"],"paper_pages":[{"arxiv_id":"2406.03537","page":"/paper/a-geometric-view-of-data-complexity-efficient"}],"arg_sig_recorded":[["gt_lid",1,"float","float64"],["pred_lid",1,"float","float64"]],"scalar_args":{},"class_bearing":false}]}]},{"bucket":[[2,"float","float64"],[2,"float","float64"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"af5570f5a1810b7a","size":1,"n_papers":1,"shape":[],"dtype":"float64","type":"float","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.0],"values_recorded":1,"members":[{"code_sha256_prefix":"202bb2cafdd7e03f","path":"src/kiv.py","papers":["2006.06366"],"paper_pages":[{"arxiv_id":"2006.06366","page":"/paper/a-class-of-algorithms-for-general"}],"arg_sig_recorded":[["x",2,"float","float64"],["y",2,"float","float64"]],"scalar_args":{},"class_bearing":false}]}]}]}