12,933 research outputs found
Mathematical open problems in Projected Entangled Pair States
Projected Entangled Pair States (PEPS) are used in practice as an efficient
parametrization of the set of ground states of quantum many body systems. The
aim of this paper is to present, for a broad mathematical audience, some
mathematical questions about PEPS.Comment: Notes associated to the Santal\'o Lecture 2017, Universidad
Complutense de Madrid (UCM), minor typos correcte
Zero-bias autoencoders and the benefits of co-adapting features
Regularized training of an autoencoder typically results in hidden unit
biases that take on large negative values. We show that negative biases are a
natural result of using a hidden layer whose responsibility is to both
represent the input data and act as a selection mechanism that ensures sparsity
of the representation. We then show that negative biases impede the learning of
data distributions whose intrinsic dimensionality is high. We also propose a
new activation function that decouples the two roles of the hidden layer and
that allows us to learn representations on data with very high intrinsic
dimensionality, where standard autoencoders typically fail. Since the decoupled
activation function acts like an implicit regularizer, the model can be trained
by minimizing the reconstruction error of training data, without requiring any
additional regularization
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