Research map / Architectures and efficiency / RNNs and normalization
Direct Feedback Alignment Provides Learning in Deep Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add dropout to 2x800 Tanh network (ablation) | DFA · MNIST | test error (%): 1.74 -> 1.45 | Helped |
| Add adversarial regularization to 2x800 Tanh network (ablation) | DFA · MNIST | test error (%): 1.74 -> 1.02 | Helped |
| Increase depth to 100x240 Tanh (ablation) | DFA vs BP/FA · MNIST | convergence | Helped |
| Add dropout to 3x1000 Tanh (ablation) | DFA · CIFAR-10 | test error (%): 47.4 -> 42.9 | Helped |
| Add dropout to 3x1000 Tanh (ablation) | FA · CIFAR-10 | test error (%): 47.0 -> 46.9 | No clear effect |
| Add dropout to 3x1000 Tanh (ablation) | DFA · CIFAR-100 | test error (%): 75.9 -> 73.1 | Helped |
| Add dropout to 3x1000 Tanh (ablation) | FA · CIFAR-100 | test error (%): 75.3 -> 75.3 | No clear effect |
| Use convolutional network instead of fully connected | DFA/FA vs BP · CIFAR-10 | test error (%): 22.5 -> 26.9 | Hurt |
| Use convolutional network instead of fully connected | DFA/FA vs BP · CIFAR-100 | test error (%): 51.7 -> 59.0 | Hurt |
| Replace Tanh with ReLU in 2x800 network (ablation) | DFA · MNIST | test error (%): 1.74 -> 1.70 | No clear effect |
| Replace Tanh with Logistic in 2x800 network (ablation) | DFA · MNIST | test error (%): 1.74 -> 1.75 | No clear effect |
| Increase depth from 1x800 to 4x800 (ablation) | BP · MNIST | test error (%): 1.59 -> 1.92 | Hurt |
Closest papers by meaning
- Overcoming Rank Collapse in Feedback Alignment (NeurIPS 2026 · similarity 0.90)
- One-Time Soft Alignment Enables Resilient Learning without Weight Transport (NeurIPS 2026 · similarity 0.89)
- Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization (ICLR 2024 · similarity 0.89)
- Pretraining with Random Noise for Fast and Robust Learning without Weight Transport (NeurIPS 2024 · similarity 0.88)
- Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks (ICML 2024 · similarity 0.87)
- Efficient Target Propagation by Deriving Analytical Solution (AAAI 2024 · similarity 0.86)
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