Inverse statistical problems: from the inverse Ising problem to data science
HC Nguyen, R Zecchina, J Berg - Advances in Physics, 2017 - Taylor & Francis
Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …
different fields, in particular high-throughput experiments in biology. In inverse problems, the …
Data based identification and prediction of nonlinear and complex dynamical systems
The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …
data or time series is central to many scientific disciplines including physical, biological …
Inverse statistical physics of protein sequences: a key issues review
In the course of evolution, proteins undergo important changes in their amino acid
sequences, while their three-dimensional folded structure and their biological function …
sequences, while their three-dimensional folded structure and their biological function …
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 …
Efficiently learning Ising models on arbitrary graphs
G Bresler - Proceedings of the forty-seventh annual ACM …, 2015 - dl.acm.org
graph underlying an Ising model from iid samples. Over the last fifteen years this problem
has been of significant interest in the statistics, machine learning, and statistical physics …
has been of significant interest in the statistics, machine learning, and statistical physics …
Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data
A data-science approach to solving the ill-conditioned inverse problem for analytical
continuation is proposed. The root of the problem lies in the fact that even tiny noise of …
continuation is proposed. The root of the problem lies in the fact that even tiny noise of …
Optimal structure and parameter learning of Ising models
Reconstruction of the structure and parameters of an Ising model from binary samples is a
problem of practical importance in a variety of disciplines, ranging from statistical physics …
problem of practical importance in a variety of disciplines, ranging from statistical physics …
Improving contact prediction along three dimensions
Correlation patterns in multiple sequence alignments of homologous proteins can be
exploited to infer information on the three-dimensional structure of their members. The …
exploited to infer information on the three-dimensional structure of their members. The …
Parallel learning by multitasking neural networks
Parallel learning, namely the simultaneous learning of multiple patterns, constitutes a
modern challenge for neural networks. While this cannot be accomplished by standard …
modern challenge for neural networks. While this cannot be accomplished by standard …
Sparse generative modeling via parameter reduction of Boltzmann machines: application to protein-sequence families
Boltzmann machines (BMs) are widely used as generative models. For example, pairwise
Potts models (PMs), which are instances of the BM class, provide accurate statistical models …
Potts models (PMs), which are instances of the BM class, provide accurate statistical models …