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diff --git a/results/figs/figure1_pareto.pdf b/results/figs/figure1_pareto.pdf Binary files differnew file mode 100644 index 0000000..5c60e85 --- /dev/null +++ b/results/figs/figure1_pareto.pdf diff --git a/results/figs/figure1_pareto.png b/results/figs/figure1_pareto.png Binary files differnew file mode 100644 index 0000000..71cae68 --- /dev/null +++ b/results/figs/figure1_pareto.png diff --git a/results/figs/figure2_scaling.pdf b/results/figs/figure2_scaling.pdf Binary files differnew file mode 100644 index 0000000..0d4a16d --- /dev/null +++ b/results/figs/figure2_scaling.pdf diff --git a/results/figs/figure2_scaling.png b/results/figs/figure2_scaling.png Binary files differnew file mode 100644 index 0000000..446a839 --- /dev/null +++ b/results/figs/figure2_scaling.png diff --git a/results/figs/main_figure_captions.md b/results/figs/main_figure_captions.md new file mode 100644 index 0000000..899fadf --- /dev/null +++ b/results/figs/main_figure_captions.md @@ -0,0 +1,24 @@ +# Main figure captions + +**Figure 1 | Accuracy–cost trade-offs for local learning.** Large points and error bars show mean ± +sample standard deviation across five initialization seeds; small translucent points show the +individual seeds. Wall time was measured on a single GTX 1080. **a,** CIFAR-10 test accuracy versus +wall time for width-64 residual MLPs trained for +five epochs. The black line is the mean-based nondominated frontier among local-learning methods. +BP is a nonlocal reference and is excluded from frontier construction. Labels give hidden depth. +**b,** SDIL versus canonical equilibrium propagation (EP) on exact 784–500–10 (d1) and +784–500–500–10 (d2) forward architectures and the same first 50,000 MNIST training examples. +Within each matched architecture, every method uses the same budget: 25 epochs for d1 and 60 epochs +for d2. EP retains its published raw-pixel, hard-sigmoid energy dynamics and relaxation +schedule; SDIL retains its z-scored, tanh feedforward dynamics. Thus architecture, examples, and +epochs are matched, but preprocessing and state dynamics are intentionally method-native. EP +training particles persist across presentations as published; test particles are freshly +zero-initialized and run for the full free-phase schedule at each evaluation. + +**Figure 2 | Credit-assignment scaling in deep local-learning networks.** Mean ± sample standard +deviation across five seeds on the same CIFAR-10 width-64 residual MLP protocol. **a,** Test +accuracy versus hidden depth. **b,** Paired accuracy gap from the exact-BP model with the same +depth and initialization seed. **c,** Mean per-sample cosine between the teaching signal and exact +descent direction, averaged over the earliest third of hidden layers. Exact gradients are used only +for this diagnostic, never for local learning. “Scaling” here means preserving useful accuracy and +credit assignment as depth grows; flattened-CIFAR accuracy itself does not increase with depth. diff --git a/results/figs/main_figure_manifest.json b/results/figs/main_figure_manifest.json new file mode 100644 index 0000000..df52456 --- /dev/null +++ b/results/figs/main_figure_manifest.json @@ -0,0 +1,1975 @@ +{ + "strict": true, + "required_seeds": [ + 0, + 1, + 2, + 3, + 4 + ], + "protocols": { + "cifar_scaling": "width64 residual tanh; 5 epochs; d5/10/20/30/60", + "ep_comparison": "exact d1/d2 width500; canonical EP dynamics" + }, + "missing_cells": [], + "sources": [ + { + "path": "results/ep_archmatch_v1_mnist_bp_w500_d1_s0.json", + "sha256": "bd9745e9d06a69709bf472c58264a5c1c63f4ff97725412611b6189ca39cd837", + "git_commit": "262c6d9ef3f0461218c1a5e632fc7dd6357e0a20", + "method": "BP", + "depth": 1, + "seed": 0 + }, + { + "path": 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\ No newline at end of file |
