{"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/get-one-hot","entry":"get_one_hot","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":6,"n_papers":6,"n_buckets":2,"n_distinct_outputs":3,"n_class_bearing":0,"not_compared":{"not_run_on_shared_input":{"n":1,"by_error":{"IndexError":1}},"no_array_argument_ran_on_own_fixture_arguments_only":{"n":1,"n_papers":1,"recorded_shared_digest_equals_own_fixture_digest":1},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/get-one-hot","buckets":[{"bucket":[[1,"int","int64"]],"n_implementations_compared":5,"n_papers":5,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"7bcb5c10457d8721","size":4,"n_papers":4,"shape":[8,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":4,"torch_reference_conventions_with_this_digest":[],"values":[0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0],"values_recorded":32,"members":[{"code_sha256_prefix":"8248ed8af5d57be5","path":"LGA.py","papers":["2302.00903"],"paper_pages":[{"arxiv_id":"2302.00903","page":"/paper/no-one-left-behind-real-world-federated-class"}],"arg_sig_recorded":[["target",1,"int","int64"]],"scalar_args":{"num_class":"4","device":"'cpu'"},"class_bearing":false},{"code_sha256_prefix":"a03c85ae21c3b667","path":"src/methods/tim.py","papers":["2204.11181"],"paper_pages":[{"arxiv_id":"2204.11181","page":"/paper/realistic-evaluation-of-transductive-few-shot-1"}],"arg_sig_recorded":[["y_s",1,"int","int64"]],"scalar_args":{},"class_bearing":false},{"code_sha256_prefix":"dc209a0ea5b09a3f","path":"LwF-CIFAR100/LwF.py","papers":["1606.09282"],"paper_pages":[{"arxiv_id":"1606.09282","page":"/paper/learning-without-forgetting"}],"arg_sig_recorded":[["target",1,"int","int64"]],"scalar_args":{"num_class":"4"},"class_bearing":false},{"code_sha256_prefix":"f1ec4c5e733f7a2a","path":"src/GLFC.py","papers":["2203.11473"],"paper_pages":[{"arxiv_id":"2203.11473","page":"/paper/federated-class-incremental-learning"}],"arg_sig_recorded":[["target",1,"int","int64"]],"scalar_args":{"num_class":"4","device":"'cpu'"},"class_bearing":false}]},{"output_sha":"b3074d9999caa427","size":1,"n_papers":1,"shape":[8,5],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0],"values_recorded":40,"members":[{"code_sha256_prefix":"71a66063541d1b3b","path":"iCaRL.py","papers":["1611.07725"],"paper_pages":[{"arxiv_id":"1611.07725","page":"/paper/icarl-incremental-classifier-and"}],"arg_sig_recorded":[["target",1,"int","int64"]],"scalar_args":{"num_class":"5"},"class_bearing":false}]}]},{"bucket":[[1,"int","int64"],[2,"float","float32"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"7bcb5c10457d8721","size":1,"n_papers":1,"shape":[8,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0],"values_recorded":32,"members":[{"code_sha256_prefix":"8d46192fd331e0c3","path":"rpo/opt_utils.py","papers":["2401.17263"],"paper_pages":[{"arxiv_id":"2401.17263","page":"/paper/robust-prompt-optimization-for-defending"}],"arg_sig_recorded":[["slice_ids",1,"int","int64"],["embed_weights",2,"float","float32"]],"scalar_args":{"device":"'cpu'"},"class_bearing":false}]}]}]}