Energy-based models (EBMs) are flexible generative architectures inspired by statistical physics, but their learning and generative properties remain poorly understood. Here, we analyze a solvable EBM...

Training in machine learning generally consists in finding one model, whose parameters minimize a data-dependent loss. Yet, empirical work shows that ensemble learning, an approach in which multiple m...

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

Specific interactions between proteins and other biomolecules are ubiquitous in cellular processes. How specificity is encoded in the protein sequence and can be modified through a minimal set of conc...

Spontaneous activity is a hallmark of brain function, reflecting the underlying circuit organization. Identifying conserved structure across individuals in this self-sustained activity has remained a ...

Riboswitches are allosteric RNA molecules that change conformation upon ligand binding to regulate downstream genes. Here, the authors use Restricted Boltzmann machines trained on natural sequences to...

This summer school will cover different topics in protein and RNA design as listed below, and will focus on teaching modern modeling approaches in this domain, from statistical physics, computer scien...

Jorge FERNANDEZ-DE-COSSIO-DIAZ CNRS Junior Professor Institut de Physique Théorique Contact: jorge[dot]fdcd[at]icloud[dot]com