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Found 5 papers, 0 papers with code
Online Learning Using Only Peer Prediction
This paper considers a variant of the classical online learning problem with expert predictions.
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
We provide polynomial bounds on the number of iterations for gradient descent to approximate the least squares matrix $\Phi$, in the case where the initial hypothesis $\Theta_1 = ... = \Theta_L = I$ has excess loss bounded by a small enough constant.
Online Learning of Combinatorial Objects via Extended Formulation
We present applications of our framework to online learning of Huffman trees and permutations.
Surprising properties of dropout in deep networks
We analyze dropout in deep networks with rectified linear units and the quadratic loss.
On the Inductive Bias of Dropout
Dropout is a simple but effective technique for learning in neural networks and other settings.
