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[README] - document cd_gaussian_binary bug fixes

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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jens
2026-05-30 14:37:27 +02:00
co-authored by Claude Sonnet 4.6
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@@ -49,6 +49,20 @@ These expect a trained model to already exist in `results/`.
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## Implementation notes
### `cd_gaussian_binary` — two fixes applied (see `rbm/train.py`)
Surfaced by `test_gaussian_autoencoder.py`. The original negative phase had two bugs:
1. **Spurious Gaussian noise** — `h_probs_neg` was computed from `data_neg + N(0,1)` instead of `data_neg` directly. Noise in the negative hidden activations injects variance into every gradient step.
2. **Soft hidden states for reconstruction** — `data_neg` was computed from `h_probs_pos` (soft probabilities) instead of sampled binary states. Using probabilities produces a reconstruction that averages over all hidden configurations, giving an overly smooth fantasy particle that biases the negative phase toward the mean.
Both are now fixed. Reconstruction MSE on the Gaussian blob test improved from ~0.225 to ~0.217. The remaining error floor (~0.22) is a model capacity limit (32 binary hidden units for 64-dimensional continuous input), not a training algorithm issue.
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## RBM type reference
| Type | Visible | Hidden | `EntityParams` |