Statistical inference links data and theory in network science
The number of network science applications across many different fields has been rapidly
increasing. Surprisingly, the development of theory and domain-specific applications often …
increasing. Surprisingly, the development of theory and domain-specific applications often …
Machine learning and the physical sciences
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …
for a vast array of data processing tasks, which has entered most scientific disciplines in …
A high-bias, low-variance introduction to machine learning for physicists
Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …
research and application. The purpose of this review is to provide an introduction to the core …
Statistical mechanics of deep learning
The recent striking success of deep neural networks in machine learning raises profound
questions about the theoretical principles underlying their success. For example, what can …
questions about the theoretical principles underlying their success. For example, what can …
Restricted Boltzmann machines in quantum physics
Restricted Boltzmann machines in quantum physics | Nature Physics Skip to main content Thank
you for visiting nature.com. You are using a browser version with limited support for CSS. To …
you for visiting nature.com. You are using a browser version with limited support for CSS. To …
[BUCH][B] Random fields for spatial data modeling
DT Hristopulos - 2020 - Springer
The series aims to: present current and emerging innovations in GIScience; describe new
and robust GIScience methods for use in transdisciplinary problem solving and decision …
and robust GIScience methods for use in transdisciplinary problem solving and decision …
Network reconstruction and community detection from dynamics
TP Peixoto - Physical review letters, 2019 - APS
We present a scalable nonparametric Bayesian method to perform network reconstruction
from observed functional behavior that at the same time infers the communities present in …
from observed functional behavior that at the same time infers the communities present in …
Machine learning for condensed matter physics
E Bedolla, LC Padierna… - Journal of Physics …, 2020 - iopscience.iop.org
Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter
at the quantum and atomistic levels, and describes how these interactions result in both …
at the quantum and atomistic levels, and describes how these interactions result in both …
Solving statistical mechanics using variational autoregressive networks
We propose a general framework for solving statistical mechanics of systems with finite size.
The approach extends the celebrated variational mean-field approaches using …
The approach extends the celebrated variational mean-field approaches using …
Information perspective to probabilistic modeling: Boltzmann machines versus born machines
We compare and contrast the statistical physics and quantum physics inspired approaches
for unsupervised generative modeling of classical data. The two approaches represent …
for unsupervised generative modeling of classical data. The two approaches represent …