Algorithm for low-rank decomposition of partially symmetric tensors via flattening orthogonalization and shifted power method with global convergence proof.
Identifiability of deep poly- nomial neural networks
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For large monomial activation degree, critical points in deep fully-connected networks coincide exactly with subnetwork configurations where neurons are inactive or redundant.
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Multi-subspace power method for decomposing partially symmetric tensors
Algorithm for low-rank decomposition of partially symmetric tensors via flattening orthogonalization and shifted power method with global convergence proof.
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Singular Learning and Occam's Razor in Deep Monomial Networks
For large monomial activation degree, critical points in deep fully-connected networks coincide exactly with subnetwork configurations where neurons are inactive or redundant.