{"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/entropy-2","entry":"Entropy","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":7,"n_papers":10,"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/entropy-2","buckets":[{"bucket":[[2,"float","float32"]],"n_implementations_compared":7,"n_papers":10,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"947540360b0cd8d6","size":7,"n_papers":10,"shape":[4],"dtype":"float32","type":"Tensor","finite":false,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":7,"torch_reference_conventions_with_this_digest":[],"values":[NaN,NaN,NaN,NaN],"values_recorded":4,"members":[{"code_sha256_prefix":"94b5622f0aa7add1","path":"tar_adaptation.py","papers":["2205.04183","2406.02862","2607.17653"],"paper_pages":[{"arxiv_id":"2205.04183","page":"/paper/local-prediction-aggregation-a-frustratingly"},{"arxiv_id":"2406.02862","page":"/paper/rethinking-guidance-information-to-utilize"},{"arxiv_id":"2607.17653","page":"/paper/arxiv-2607-17653"}],"arg_sig_recorded":[["input_",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"866722884763b224","path":"code/digit/uda_digit.py","papers":["2012.07297","2605.01369"],"paper_pages":[{"arxiv_id":"2012.07297","page":"/paper/source-data-absent-unsupervised-domain"},{"arxiv_id":"2605.01369","page":"/paper/arxiv-2605-01369"}],"arg_sig_recorded":[["input_",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"54f9f530b53b6db0","path":"UODR/train_loader.py","papers":["2003.12237"],"paper_pages":[{"arxiv_id":"2003.12237","page":"/paper/towards-discriminability-and-diversity-batch"}],"arg_sig_recorded":[["input_",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"a29c88002bbdc7f5","path":"adaptation_office.py","papers":["2412.14301"],"paper_pages":[{"arxiv_id":"2412.14301","page":"/paper/what-has-been-overlooked-in-contrastive"}],"arg_sig_recorded":[["input_",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"a4fb3d7d83bd280c","path":"dda_model/dda_model.py","papers":["2107.11055"],"paper_pages":[{"arxiv_id":"2107.11055","page":"/paper/transporting-causal-mechanisms-for"}],"arg_sig_recorded":[["input_",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"ab338e78525a1a07","path":"src/trainer.py","papers":["2104.00808"],"paper_pages":[{"arxiv_id":"2104.00808","page":"/paper/curriculum-graph-co-teaching-for-multi-target"}],"arg_sig_recorded":[["input",2,"float","float32"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"e9f124f4e6ac515e","path":"adapt_da.py","papers":["2403.01966"],"paper_pages":[{"arxiv_id":"2403.01966","page":"/paper/enhancing-information-maximization-with"}],"arg_sig_recorded":[["input_",2,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]}]}