{"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/compute-entropy","entry":"compute_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":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":{"KeyError":1,"ValueError":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/compute-entropy","buckets":[{"bucket":[[2,"float","float32"]],"n_implementations_compared":2,"n_papers":2,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"435156207bcb043b","size":1,"n_papers":1,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":false,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[-1.0619845390319824,NaN,0.3533382713794708,NaN,NaN,NaN,NaN,0.3191385567188263,0.34043022990226746,0.04005217179656029,NaN,NaN,NaN,-1.9559845924377441,NaN,0.32859957218170166,0.18014708161354065,NaN,0.3625714182853699,NaN,NaN,NaN,NaN,0.016239434480667114,NaN,0.3623826801776886,0.05352196842432022,-0.01761808432638645,NaN,-0.30868831276893616,NaN,NaN],"values_recorded":32,"members":[{"code_sha256_prefix":"44f8073d78252a7a","path":"evaluate_samples.py","papers":["2201.10787"],"paper_pages":[{"arxiv_id":"2201.10787","page":"/paper/variational-model-inversion-attacks-1"}],"arg_sig_recorded":[["p",2,"float","float32"]],"scalar_args":{"epsilon":"0.0001"},"class_bearing":false}]},{"output_sha":"e0770880d670f9ad","size":1,"n_papers":1,"shape":[4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[1.4792883396148682,1.4573825597763062,1.8222516775131226,1.7319810390472412],"values_recorded":4,"members":[{"code_sha256_prefix":"90726473320040cd","path":"AdaSG_code/CIFAR/trainer.py","papers":["2302.09572"],"paper_pages":[{"arxiv_id":"2302.09572","page":"/paper/rethinking-data-free-quantization-as-a-zero"}],"arg_sig_recorded":[["p_logit",2,"float","float32"]],"scalar_args":{"T":"1.0"},"class_bearing":false}]}]},{"bucket":[[1,"float","float64"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"2399fcd7f479a73c","size":1,"n_papers":1,"shape":[],"dtype":"float64","type":"float64","finite":false,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[NaN],"values_recorded":1,"members":[{"code_sha256_prefix":"20cdfa5f66a261c8","path":"evaluate_uq_qa.py","papers":["2311.08718"],"paper_pages":[{"arxiv_id":"2311.08718","page":"/paper/decomposing-uncertainty-for-large-language"}],"arg_sig_recorded":[["vec",1,"float","float64"]],"scalar_args":{},"class_bearing":false}]}]},{"bucket":[[3,"float","float32"],[1,"int","int64"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"4fde4328a11f80b2","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":[6.352594375610352],"values_recorded":1,"members":[{"code_sha256_prefix":"d1f6db28a2a7ff6a","path":"vauq/scoring.py","papers":["2602.21054"],"paper_pages":[{"arxiv_id":"2602.21054","page":"/paper/arxiv-2602-21054"}],"arg_sig_recorded":[["image",3,"float","float32"],["generated_ids",1,"int","int64"]],"scalar_args":{"lvlm":"MockLVLM(\n  (linear): Linear(in_features=16, out_features=10","question":"'What is this?'"},"class_bearing":false}]}]},{"bucket":[[3,"float","float32"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"74999fd28ab18ccc","size":1,"n_papers":1,"shape":[],"dtype":"float32","type":"Tensor","finite":false,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[NaN],"values_recorded":1,"members":[{"code_sha256_prefix":"42b500b9cf00fc46","path":"engine/engine_vae_bias.py","papers":["2405.20790"],"paper_pages":[{"arxiv_id":"2405.20790","page":"/paper/intersectional-unfairness-discovery"}],"arg_sig_recorded":[["vector",3,"float","float32"]],"scalar_args":{"normal":"True","mask":"None"},"class_bearing":false}]}]}]}