In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks
that use binary values for activations and weights, instead of full precision values. With …

In this Review, memristors are examined from the frameworks of both von Neumann and
neuromorphic computing architectures. For the former, a new logic computational process …

Generalized linear models (GLMs) are used in high-dimensional machine learning,
statistics, communications, and signal processing. In this paper we analyze GLMs when the …

We examine a class of stochastic deep learning models with a tractable method to compute
information-theoretic quantities. Our contributions are three-fold:(i) We show how entropies …

Abstract Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-
based methods, such as BackPropagation (BP). Inference in probabilistic graphical models …

In artificial neural networks, learning from data is a computationally demanding task in which
a large number of connection weights are iteratively tuned through stochastic-gradient …

We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to
multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and …

We show that discrete synaptic weights can be efficiently used for learning in large scale
neural systems, and lead to unanticipated computational performance. We focus on the …

In this paper, we review some of the novel emerging memory technologies and how they
can enable energy-efficient implementation of large neuromorphic computing systems. We …

Heuristic tools from statistical physics have been used in the past to compute the optimal
learning and generalization errors in the teacher-student scenario in multi-layer neural …