Research map / Architectures and efficiency / RNNs and normalization
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use explicit regularization that promotes high separation rank | ResNet18 and ResNet34 · IsSameClass and Pathfinder | test accuracy | Helped |
| Train with near-zero initialization | Hierarchical tensor factorization · Tensor completion | hierarchical tensor rank | Helped |
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