Research map / Learning theory and algorithms / Matrix and tensor methods
Implicit Regularization in Tensor Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fit with low tensor rank instead of randomizing images or labels | Tensor-factorized predictors · MNIST and Fashion-MNIST | mean squared error | Helped |
| Use smaller initialization | Tensor factorization on synthetic low-rank tensor completion · Rank-5 tensor completion | number of components substantially departing from zero | Helped |
| Replace fixed small learning rate with adaptive learning rate | Tensor factorization on synthetic tensor completion · Synthetic low-rank tensor completion | learning dynamics | No clear effect |
| Use tensor factorization instead of a linear predictor | Predictors for MNIST and Fashion-MNIST · MNIST and Fashion-MNIST | mean squared error | Helped |
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