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Found 3 papers, 1 papers with code
Convergence of ease-controlled Random Reshuffling gradient Algorithms under Lipschitz smoothness
In this work, we consider minimizing the average of a very large number of smooth and possibly non-convex functions, and we focus on two widely used minibatch frameworks to tackle this optimization problem: Incremental Gradient (IG) and Random Reshuffling (RR).
Block Layer Decomposition schemes for training Deep Neural Networks
Deep Feedforward Neural Networks' (DFNNs) weights estimation relies on the solution of a very large nonconvex optimization problem that may have many local (no global) minimizers, saddle points and large plateaus.
A gray-box approach for curriculum learning
Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors.
