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A Gradient Method for Multilevel Optimization
Although application examples of multilevel optimization have already been discussed since the 1990s, the development of solution methods was almost limited to bilevel cases due to the difficulty of the problem.
Semi-flat minima and saddle points by embedding neural networks to overparameterization
We theoretically study the landscape of the training error for neural networks in overparameterized cases.
Efficient Preconditioning for Noisy Separable NMFs by Successive Projection Based Low-Rank Approximations
To ensure this, our modification employs a rank-$k$ approximation produced by an SPA based algorithm.
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