When data values are`2-normalized, our algorithm iteratively computes the top eigenvector of a sequence of matrices, and does not lose any multiplicativeapproximation factor.

Privacy issue is a main concern in developing face recognition techniques.

We present a PT AS for learning random constant-depth networks.

Autoencoder is a powerful unsupervised learning framework to learn latent representations by minimizing reconstruction loss of the input data [1]. Autoencoders have been widely used in unsupervised learning tasks such as representation learning [1] [2], denoising [3], and generative model [4][5]. Most autoencoders are under-complete autoencoders, for which the latent space is smaller than the input data [2]. Over-complete autoencoders have latent space larger than input data.

Traditional robust reinforcement learning often depends on rectangularity assumptions, where adverse probability measures of outcome states are assumed to be independent across different states and actions.

While many models have been developed for characterizing behavior in binary decision-making and bandit tasks, comparatively little work has focused onanimal decision-making inmorecomplextasks,suchasnavigationthrough a maze.

Specifically,we propose to construct amatrix-variate normal prior (on weights) whose covariance matrix hasaKroneckerproduct structure. Thisstructure is designed to capture the correlations in neurons through backpropagation.

This filter is however not invariant under translation, leading to visual artifacts and non-optimal shapes.