activity
20142026
most citedPerspective: Energy Landscapes for Machine Learning

104 citations · 543 across the 19 of their papers we have counts for

collaborators
Showing cond-mat.stat-mechShow all

8 papers · 1 filter

cond-mat.stat-mech2026

Fast generation of spectrally-shaped disorder, on the sphere

Mathias Casiulis, Stefano Martiniani

The design of disordered point patterns with desirable properties is an exciting and ongoing research endeavor, with applications ranging from materials to computer science. A succ…

cond-mat.stat-mech2024

Absorbing state dynamics of stochastic gradient descent

Guanming Zhang, Stefano Martiniani

Stochastic gradient descent (SGD) is a fundamental tool for training deep neural networks across a variety of tasks. In self-supervised learning, different input categories map to…

cond-mat.stat-mech2023★ 17 cited

Fast Generation of Spectrally-Shaped Disorder

Aaron Shih, Mathias Casiulis, Stefano Martiniani

Media with correlated disorder display unexpected transport properties, but it is still a challenge to design structures with desired spectral features at scale. In this work, we i…

cond-mat.stat-mech2020★ 14 cited

Vicsek Model by Time-Interlaced Compression: a Dynamical Computable Information Density

Andrea Cavagna, Paul M. Chaikin, Dov Levine +3

Collective behavior, both in real biological systems as well as in theoretical models, often displays a rich combination of different kinds of order. A clear-cut and unique definit…

cond-mat.stat-mech2020★ 13 cited

Correlation lengths in the language of computable information

Stefano Martiniani, Yuval Lemberg, Paul M. Chaikin +1

Computable Information Density (CID), the ratio of the length of a losslessly compressed data file to that of the uncompressed file, is a measure of order and correlation in both e…

cond-mat.stat-mech2016★ 18 cited

Monte Carlo sampling for stochastic weight functions

Daan Frenkel, K. Julian Schrenk, Stefano Martiniani

Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial move is decided by comparing a computed acceptance probability with a random number,…

cond-mat.stat-mech2015★ 60 cited

Turning intractable counting into sampling: computing the configurational entropy of three-dimensional jammed packings

Stefano Martiniani, K. Julian Schrenk, Jacob D. Stevenson +2

We report a numerical calculation of the total number of disordered jammed configurations of repulsive, three-dimensional spheres in a fixed volume . To make these calcu…

cond-mat.stat-mech2014★ 25 cited

Superposition Enhanced Nested Sampling

Stefano Martiniani, Jacob D. Stevenson, David J. Wales +1

The theoretical analysis of many problems in physics, astronomy and applied mathematics requires an efficient numerical exploration of multimodal parameter spaces that exhibit brok…