Home › Census › cross_entropy

cross_entropy

Syntologyfunction-name censuscensus 2026-09-22battery b986f7e04d79all samples with this name

cross_entropy: 6 implementations from 8 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 5 distinct outputs across 2 buckets, one shared input per bucket.

Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence. Implementations are compared only within one bucket, the 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; each bucket's shared input is fixed by that key, so members of one bucket saw bitwise-identical inputs under their own scalar arguments. A cluster is the set of members whose recorded output digest is identical. Nothing here says which computation a paper's method intended, and nothing reproduces a paper's results.

Not compared, and not in the tables or the counts above:

Bucket 1 of 2: arg 1: rank 2, kind float, dtype float32 · arg 2: rank 2, kind float, dtype float32

5 implementations from 7 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 4 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.

Cluster (same digest to six decimals)MembersShared output on this bucket's input
2 implementations
2 papers
2399fcd7f479
recorded values identical

utils/losses.py 35d4ddee
recorded proba_pred:2/float/float32, proba_target:2/float/float32

mDLAM.py 7671bae0
recorded label:2/float/float32, prob:2/float/float32
[nan]
shape [] · float32 · Tensor · non-finite
1 implementation
3 papers
f8000e4c2112
one code sha held from 3 papers' repositories
CLIP.py 63e6e57b
recorded preds:2/float/float32, targets:2/float/float32; reduction='none'
[-13.75, -5.2393, -10.401, -8.64224]
shape [4] · float32 · Tensor
1 implementation
1 paper
ba10c57de7de

UGformerV2_PyTorch/train_UGformerV2.py 339e9f82
recorded pred:2/float/float32, soft_targets:2/float/float32
[-9.50814]
shape [] · float32 · Tensor
1 implementation
1 paper
fbf16c41c2a0

ood_training.py d26b88cd
recorded logits:2/float/float32, targets:2/float/float32
[-1.18852]
shape [] · float32 · Tensor

Bucket 2 of 2: arg 1: rank 1, kind float, dtype float64 · arg 2: rank 1, kind float, dtype float64

1 implementation from 1 paper share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 1 distinct output, largest cluster first. Values are the first 8 of the recorded output, flattened.

Cluster (same digest to six decimals)MembersShared output on this bucket's input
1 implementation
1 paper
af5570f5a181

visualize_atari/saliency.py 636e79de
recorded L_policy:1/float/float64, l_policy:1/float/float64; L_idx=2
[0]
shape [] · float64

Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence; where a cluster's members carry recorded values, the largest difference among them is shown under the cluster. Paper titles are the archive's archive 2025-07-28 where the paper is in the archive and the graph's where it was added by Syntology; papers with no page here are shown by their recorded paper id only. A paper count above the implementation count means one implementation (one code sha) is held from several papers' repositories and counts once. Per-sample status, licence and fingerprint records for each paper are on its paper page. JSON twin: /census/cross-entropy.json.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections