{"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/gaussian-kernel","entry":"gaussian_kernel","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":9,"n_papers":9,"n_buckets":6,"n_distinct_outputs":9,"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":2,"n_papers":4,"recorded_shared_digest_equals_own_fixture_digest":2},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/gaussian-kernel","buckets":[{"bucket":[[2,"float","float32"],[2,"float","float32"],[0,"float","float32"]],"n_implementations_compared":2,"n_papers":2,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"26e83ea82d468c16","size":1,"n_papers":1,"shape":[4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[1.0,0.005583538673818111,0.3649054765701294,0.005671566817909479,0.005583538673818111,1.0,0.005053684581071138,0.028459765017032623,0.3649054765701294,0.005053684581071138,1.0,0.012272468768060207,0.005671566817909479,0.028459765017032623,0.012272468768060207,1.0],"values_recorded":16,"members":[{"code_sha256_prefix":"1c4221b5f7ba7e83","path":"RAMEN/models/ramen.py","papers":["2503.14459"],"paper_pages":[{"arxiv_id":"2503.14459","page":"/paper/doubly-robust-identification-of-treatment"}],"arg_sig_recorded":[["xi",2,"float","float32"],["xj",2,"float","float32"],["sigma",0,"float","float32"]],"scalar_args":{},"class_bearing":false}]},{"output_sha":"cce4610e8a217ad8","size":1,"n_papers":1,"shape":[4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[1.0,2606233.25,17.6505069732666,2492688.25,2606233.25,1.0,3461946.5,25223.669921875,17.6505069732666,3461946.5,1.0,276727.71875,2492688.25,25223.669921875,276727.71875,1.0],"values_recorded":16,"members":[{"code_sha256_prefix":"dc6fed6be8b7877e","path":"SWAE/xp_swae.py","papers":["2206.08780"],"paper_pages":[{"arxiv_id":"2206.08780","page":"/paper/spherical-sliced-wasserstein"}],"arg_sig_recorded":[["x",2,"float","float32"],["y",2,"float","float32"],["h",0,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]},{"bucket":[[2,"float","float32"],[2,"float","float32"]],"n_implementations_compared":2,"n_papers":2,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"26c85e34ff6ea7ef","size":1,"n_papers":1,"shape":[4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[1.0,0.09576158970594406,0.6339069604873657,0.09644132852554321,0.09576158970594406,1.0,0.09154003858566284,0.20000004768371582,0.6339069604873657,0.09154003858566284,1.0,0.13672500848770142,0.09644132852554321,0.20000004768371582,0.13672500848770142,1.0],"values_recorded":16,"members":[{"code_sha256_prefix":"447cd6e519ab757f","path":"utils/kernels.py","papers":["2405.18997"],"paper_pages":[{"arxiv_id":"2405.18997","page":"/paper/kernel-semi-implicit-variational-inference"}],"arg_sig_recorded":[["samples_x",2,"float","float32"],["samples_y",2,"float","float32"]],"scalar_args":{"h":"-1","get_width":"False","detach":"False"},"class_bearing":false}]},{"output_sha":"60a04014ec19546c","size":1,"n_papers":1,"shape":[4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[1.0,7.323522344115929e-10,0.016781700775027275,7.803049317800514e-10,7.323522344115929e-10,1.0,4.888232596478304e-10,5.402507667895406e-07,0.016781700775027275,4.888232596478304e-10,1.0,1.784304792806779e-08,7.803049317800514e-10,5.402507667895406e-07,1.784304792806779e-08,1.0],"values_recorded":16,"members":[{"code_sha256_prefix":"4bd0b303457583b9","path":"Ablation/nn_bandwidth/Kernels.py","papers":["2509.25507"],"paper_pages":[{"arxiv_id":"2509.25507","page":"/paper/arxiv-2509-25507"}],"arg_sig_recorded":[["A",2,"float","float32"],["B",2,"float","float32"]],"scalar_args":{"sigma":"1.0"},"class_bearing":false}]}]},{"bucket":[[2,"float","float32"]],"n_implementations_compared":2,"n_papers":2,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"34cc6a9f8535bccc","size":1,"n_papers":1,"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":[4.093784339298523e-12,5.353320453593824e-09,0.13018421828746796,0.017520379275083542,0.001174527918919921,0.0001631633349461481,0.023758677765727043,0.057762954384088516,0.09258301556110382,0.000507773831486702,0.22583934664726257,2.2699229162981283e-09,0.4099125862121582,1.4708099764275092e-19,0.09576943516731262,0.07061976939439774,0.770236074924469,5.64211122267011e-10,0.17914612591266632,0.21316538751125336,0.7946326732635498,1.1424290278228e-05,0.079530730843544,0.00034681797842495143,0.00504282396286726,0.17770950496196747,0.0006300855311565101,0.00020173843950033188,4.803353931492893e-06,1.8233353102914407e-06,6.727067329848069e-07,0.04738182574510574],"values_recorded":32,"members":[{"code_sha256_prefix":"cb509c068da815e4","path":"overcomplete/sae/jump_sae.py","papers":["2503.01822"],"paper_pages":[{"arxiv_id":"2503.01822","page":"/paper/projecting-assumptions-the-duality-between"}],"arg_sig_recorded":[["x",2,"float","float32"]],"scalar_args":{"bandwith":"0.5"},"class_bearing":false}]},{"output_sha":"6236892e74fb6f23","size":1,"n_papers":1,"shape":[4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[1.0,0.48249104619026184,0.8679521083831787,0.4835524559020996,0.48249104619026184,1.0,0.475780189037323,0.6065306663513184,0.8679521083831787,0.475780189037323,1.0,0.538934588432312,0.4835524559020996,0.6065306663513184,0.538934588432312,1.0],"values_recorded":16,"members":[{"code_sha256_prefix":"1f9ef96229ba4b75","path":"sispca/model.py","papers":["2410.23595"],"paper_pages":[{"arxiv_id":"2410.23595","page":"/paper/disentangling-interpretable-factors-with"}],"arg_sig_recorded":[["x",2,"float","float32"]],"scalar_args":{"bw":"None"},"class_bearing":false}]}]},{"bucket":[[1,"float","float32"],[0,"float","float32"],[2,"float","float32"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"2f84fa4fd5d50b06","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":[0.035240188241004944,0.05279882624745369,0.12171611934900284,0.2211640626192093],"values_recorded":4,"members":[{"code_sha256_prefix":"abf7adc7e3aac64a","path":"mta.py","papers":["2405.02266"],"paper_pages":[{"arxiv_id":"2405.02266","page":"/paper/on-the-test-time-zero-shot-generalization-of"}],"arg_sig_recorded":[["mu",1,"float","float32"],["bandwidth",0,"float","float32"],["datapoints",2,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]},{"bucket":[[1,"float","float32"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"2c17218ed2d64574","size":1,"n_papers":1,"shape":[],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[5.4120759159559384e-05],"values_recorded":1,"members":[{"code_sha256_prefix":"375fe0e2f099808a","path":"KMIFQE.py","papers":["2405.18792"],"paper_pages":[{"arxiv_id":"2405.18792","page":"/paper/kernel-metric-learning-for-in-sample-off"}],"arg_sig_recorded":[["u",1,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]},{"bucket":[[2,"float","float32"],[0,"float","float32"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"26e83ea82d468c16","size":1,"n_papers":1,"shape":[4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[1.0,0.005583533551543951,0.3649054765701294,0.005671561695635319,0.005583533551543951,1.0,0.0050536771304905415,0.028459765017032623,0.3649054765701294,0.0050536771304905415,1.0,0.01227246318012476,0.005671561695635319,0.028459765017032623,0.01227246318012476,1.0],"values_recorded":16,"members":[{"code_sha256_prefix":"c345cceb5f51f7cb","path":"our_method/lisc_linear.py","papers":["2106.03340"],"paper_pages":[{"arxiv_id":"2106.03340","page":"/paper/instrument-space-selection-for-kernel-maximum"}],"arg_sig_recorded":[["z",2,"float","float32"],["p",0,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]}]}