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The basic underlying assumption of machine learning (ML) models is that the training and test data are sampled from the same distribution. However, in daily practice, this assumption is often broken, i.e. the distribution of the test data…

Computation and Language · Computer Science 2026-01-16 Adriana Valentina Costache , Silviu Florin Gheorghe , Eduard Gabriel Poesina , Paul Irofti , Radu Tudor Ionescu

We review several models of glassy systems where the randomness is self generated, i.e. already an infinitesimal amount of disorder is sufficient to cause a transition to a non-ergodic, glassy state. We discuss the application of the…

Disordered Systems and Neural Networks · Physics 2011-01-05 Maxim Dzero , Joerg Schmalian , Peter G. Wolynes

A spin-glass system with a smooth or fractal outer surface is studied by renormalization-group theory, in bulk spatial dimension d=3. Independently varying the surface and bulk random-interaction strengths, phase diagrams are calculated.…

Disordered Systems and Neural Networks · Physics 2025-02-18 Yiğit Ertaç Pektaş , E. Can Artun , A. Nihat Berker

This thesis includes analysis of disordered spin ensembles corresponding to Exact Cover, a multi-access channel problem, and composite models combining sparse and dense interactions. The satisfiability problem in Exact Cover is addressed…

Disordered Systems and Neural Networks · Physics 2009-05-22 Jack Raymond

We construct and analyze a family of $M$-component vectorial spin systems which exhibit glass transitions and jamming within supercooled paramagnetic states without quenched disorder. Our system is defined on lattices with connectivity…

Statistical Mechanics · Physics 2018-06-27 Hajime Yoshino

The driven transport of plastic systems in various disordered backgrounds is studied within mean field theory. Plasticity is modeled using non-convex interparticle potentials that allow for phase slips. This theory most naturally describes…

Disordered Systems and Neural Networks · Physics 2009-11-10 Karl Saunders , J. M. Schwarz , M. Cristina Marchetti , A. Alan Middleton

We establish relations between different characterizations of order in spin glass models. We first prove that the broadening of the replica overlap distribution indicated by a nonzero standard deviation of the replica overlap $R^{1,2}$…

Mathematical Physics · Physics 2024-02-27 Chigak Itoi , Hisamitsu Mukaida , Hal Tasaki

Uncertainty quantification requires efficient summarization of high- or even infinite-dimensional (i.e., non-parametric) distributions based on, e.g., suitable point estimates (modes) for posterior distributions arising from model-specific…

Statistics Theory · Mathematics 2024-04-10 Christian Clason , Tapio Helin , Remo Kretschmann , Petteri Piiroinen

We introduce a spectral approach to characterizing the three-dimensional Edwards-Anderson spin glass. By analyzing the eigenvalue statistics of overlap matrices constructed from two-dimensional cross-sections, we identify a crossover from…

Disordered Systems and Neural Networks · Physics 2026-03-05 Yaprak Onder , Abbas Ali Saberi , Roderich Moessner

The three-dimensional Edwards-Anderson and mean-field Sherrington-Kirkpatrick Ising spin glasses are studied via large-scale Monte Carlo simulations at low temperatures, deep within the spin-glass phase. Performing a careful statistical…

Disordered Systems and Neural Networks · Physics 2012-10-29 B. Yucesoy , Helmut G. Katzgraber , J. Machta

We present here a general framework, expressed by a system of nonlinear differential equations, suitable for the modelling of taxation and redistribution in a closed (trading market) society. This framework allows to describe the evolution…

Physics and Society · Physics 2011-09-06 Maria Letizia Bertotti , Giovanni Modanese

We present a technique to generate relations connecting pure state weights, overlaps, and correlation functions in short-range spin glasses. These are obtained directly from the unperturbed Hamiltonian and hold for general coupling…

Disordered Systems and Neural Networks · Physics 2015-11-04 L. -P. Arguin , C. M. Newman , D. L. Stein

Symmetries represent a fundamental constraint for physical systems and relevant new phenomena often emerge as a consequence of their breaking. An important example is provided by space- and time-translational invariance in statistical…

Statistical Mechanics · Physics 2012-12-21 Matteo Marcuzzi , Andrea Gambassi , Michel Pleimling

Methods for understanding classical disordered spin systems with interactions conforming to some idealized graphical structure are well developed. The equilibrium properties of the Sherrington-Kirkpatrick model, which has a densely…

Disordered Systems and Neural Networks · Physics 2013-05-29 Jack Raymond , David Saad

Diffusion models have made significant advances recently in high-quality image synthesis and related tasks. However, diffusion models trained on real-world datasets, which often follow long-tailed distributions, yield inferior fidelity for…

Computer Vision and Pattern Recognition · Computer Science 2024-02-19 Divin Yan , Lu Qi , Vincent Tao Hu , Ming-Hsuan Yang , Meng Tang

We present a technique for constructing suitable posterior probability distributions in situations for which the sampling distribution of the data is not known. This is very useful for modern scientific data analysis in the era of "big…

Instrumentation and Methods for Astrophysics · Physics 2017-08-30 Steven Gratton

In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information…

Machine Learning · Computer Science 2012-07-03 Abhishek Kumar , Hal Daume

Broken-symmetry-induced order parameters account for many phenomena in condensed matter physics. For spin glasses, such a framework dictates its theoretical construction, whereas experiments have only established dynamical behaviors such as…

Disordered Systems and Neural Networks · Physics 2026-05-25 Margarita G. Dronova , Feng Ye , Zachary J. Morgan , Yishu Wang , Yejun Feng

We present a new field theoretic approach for finite dimensional site disordered spin systems by introducing the notion of grand canonical disorder, where the number of spins in the system is random but quenched. We analyze this field…

Disordered Systems and Neural Networks · Physics 2009-10-28 David S. Dean , David Lancaster

Infiltration of diffusing particles from one material to another where the diffusion mechanism is either normal or anomalous is a widely observed phenomena. When the diffusion is anomalous we find interesting behaviors: diffusion may lead…

Statistical Mechanics · Physics 2010-05-04 Nickolay Korabel , Eli Barkai
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