trunc_normal_
trunc_normal_: 37 implementations from 54 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 4 distinct outputs.
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:
- Every examined implementation of this name that took an array argument ran on the shared input.
- No implementation of this name ran on its own fixture arguments only: every one that ran took an array argument.
- Every compared output was digested.
Bucket 1 of 1: arg 1: rank 2, kind float, dtype float32
37 implementations from 54 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) | Members | Shared output on this bucket's input |
|---|---|---|
| 34 implementations 50 papers 42f0dd179468 recorded values identical |
real/test_code/architecture/MST.py c76d1867 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
architectures/backbone/DINO_ViT.py 915b71e7 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
model/SSRT.py 02566da6 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
corun_colabator/archs/corun_arch.py 056f3716 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
models/FM/EEGPT/Model_EEGPT.py 359975e3 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
fastreid/modeling/backbones/vision_transformer.py 605878f6 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
easy_ViTPose/vit_models/model.py c3487124 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
src/model/model_action.py d8578a1e recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 one code sha held from 2 papers' repositories model/impl/actionformer.py e6ea9664 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
models/cha_mae_vit.py fabe0899 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 models/early_exit.py 04c04dfd recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 basicsr/archs/swinir_arch.py 0af416f1 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 models/jepa.py 28455f55 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 easycv/models/backbones/vision_transformer.py 462cf9fa recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
src/models/predictor.py 4e02bf1d recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 real/test_code/architecture/DAUHST.py 505222cf recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 split_model.py 6586759c recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 models/network_vrt.py 8347c57b recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
modeling/fusion_part/CRM.py 8c337449 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 foundation/model.py 95f8054d recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 vissl/models/trunks/beit_transformer.py ab8464b4 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 towhee/models/swin_transformer/model.py ada91a01 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 src/models/reefl_vit.py af8aefa3 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0
models/sam_withToken.py b425e248 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 model/backbones/vit_pytorch.py ba6277ed recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 models/dichavit.py be2feea8 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 timm/models/bixt.py c92dcfe5 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 models/vision_transformer_moe.py ca342573 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 dic_models.py cd079d4c recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 pypots/nn/modules/tslanet/backbone.py d347dc5f recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 joint_evol_opt.py ef920352 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 towhee/models/uniformer/uniformer.py f3e47c6e recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 codes/models/decoder_affordance.py f836ffb1 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 models.py fd05acd7 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 |
[-0.00895651, 0.69355, -1.24155, -1.04171, -0.478393, 0.326503, -0.0237045, 1.16706, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 2 papers 8983255eae79 |
modules/fusion_module.py 5cb5985f recorded x:2/float/float32; mean=0.0, std=1.0 |
[-1.12584, -1.15236, -0.250579, -0.433879, 0.84871, 0.692009, -0.316013, -0.11522, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 1 paper 10d0186bfd20 |
pbb/models.py 92221940 recorded tensor:2/float/float32; mean=0.0, std=1.0, a=-2.0, b=2.0 |
[-0.00895653, 0.69355, -1.24155, -1.04171, -0.478393, 0.326503, -0.0237045, 1.16706, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 1 paper 8be0b5c837c2 |
models/modeling/PSVMAModel/PSVMANet.py 49e6743e recorded tensor:2/float/float32; mean=0, std=0.01 |
[-0.0112584, 0.0084871, 0.00322275, 0.00119842, -0.0135265, 0.00598839, 0.00750189, 0.0138937, …] shape [4, 8] · float32 · Tensor |
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/trunc-normal.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