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 …

Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

The problem of optimizing over random structures emerges in many areas of science and
engineering, ranging from statistical physics to machine learning and artificial intelligence …

Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional
(3D) structure hence leaves an echo of correlations in the evolutionary record. Reverse …

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

We elaborate on a linear-time implementation of Collective-Influence (CI) algorithm
introduced by Morone, Makse, Nature 524, 65 (2015) to find the minimal set of influencers in …

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 …

Regulatory network reconstruction is a fundamental problem in computational biology.
There are significant limitations to such reconstruction using individual datasets, and …

Network alignment generalizes and unifies several approaches for forming a matching or
alignment between the vertices of two graphs. We study a mathematical programming …

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 …