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| author | YurenHao0426 <Blackhao0426@gmail.com> | 2026-07-27 14:51:17 -0500 |
|---|---|---|
| committer | YurenHao0426 <Blackhao0426@gmail.com> | 2026-07-27 14:51:17 -0500 |
| commit | a5a04b29bc93bdee4dbce6fa46bfda7cfe843928 (patch) | |
| tree | 6cfabf8daa2fbbf4bdb786c0a604ddf9729c7da1 /results/figs/figure6_standard_depth_scaling_caption.md | |
| parent | a2df73afd68278bbc961a49bbc722d17b4d83ef0 (diff) | |
figure: audit standard-depth SDIL scaling
Diffstat (limited to 'results/figs/figure6_standard_depth_scaling_caption.md')
| -rw-r--r-- | results/figs/figure6_standard_depth_scaling_caption.md | 18 |
1 files changed, 18 insertions, 0 deletions
diff --git a/results/figs/figure6_standard_depth_scaling_caption.md b/results/figs/figure6_standard_depth_scaling_caption.md new file mode 100644 index 0000000..62ae132 --- /dev/null +++ b/results/figs/figure6_standard_depth_scaling_caption.md @@ -0,0 +1,18 @@ +# Standard-depth ResNet scaling figure caption + +**Figure 6 | Dynamic somato-dendritic innovation scales across standard +ResNet depth.** All points are frozen CIFAR-10 test endpoints from seeds +10--14 after 200 epochs; error bars show 95% normal intervals over paired +seeds. The renderer validates all 60 records and their source hashes against +the passed predeclared gate. **a,** BP, clean reciprocal Kolen--Pollack credit, +and dynamic SDIL under four-times-RMS soma-predictable apical traffic all gain +accuracy from ResNet-20 to ResNet-56. SDIL gains 1.176 points, and all five +paired seeds improve. **b,** On the full accuracy scale, fixed direct feedback +alignment remains far below both reciprocal local learners at every depth; +the complete nine-method crossover is reported separately rather than inferred +from this four-method panel. **c,** Paired ResNet-56 minus ResNet-20 gains for +BP and SDIL. **d,** SDIL retains a mean early-third teaching-signal cosine +above 0.9994 while its hardware-independent affine-MAC estimate remains below +1.34 times matched BP. Exact gradients are diagnostic-only and never used for +learning. The SDIL rule uses no task-loss queries but does use one explicitly +counted instruction-off neutral observation per ordinary example. |
