Research map / Learning theory and algorithms / Matrix and tensor methods
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase depth of deep CP factorization from 0 to 5 | deep CP tensor factorization · Synthetic 10^4 tensor, 20% observed | effective CP-rank of learned tensor | Helped |
| increase depth (ablation) | deep CP factorization · Synthetic, 75-90% missing | best validation loss | Mixed |
| use small-variance initialization vs large initialization | deep CP factorization · Synthetic | convergence to low-rank solution | Mixed |
| increase depth | deep CP factorization · Synthetic, generalization loss across initializations | robustness to initialization | Helped |
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- Factor Augmented Tensor-on-Tensor Neural Networks (AAAI 2025 · similarity 0.87)
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