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Sparse autoencoders (SAEs) have become a central tool for interpreting language models. However, two key SAE analyses that remain difficult to scale are (1) matching semantically similar features across multi-layers and (2) compressing…

Machine Learning · Computer Science 2026-05-28 Tue M. Cao , Nguyen Do , My T. Thai

This paper proposes an original Riemmanian geometry for low-rank structured elliptical models, i.e., when samples are elliptically distributed with a covariance matrix that has a low-rank plus identity structure. The considered geometry is…

Differential Geometry · Mathematics 2020-01-07 Florent Bouchard , Arnaud Breloy , Guillaume Ginolhac , Alexandre Renaux , Frédéric Pascal

In this work, a complete error analysis is presented for fully discrete solutions of the subdiffusion equation with a time-dependent diffusion coefficient, obtained by the Galerkin finite element method with conforming piecewise linear…

Numerical Analysis · Mathematics 2018-09-24 Bangti Jin , Buyang Li , Zhi Zhou

We develop a convex framework for spatially varying coefficient quantile regression that, for each predictor, separates a location-invariant \emph{global} effect from a \emph{spatial deviation}. An adaptive group penalty selects whether a…

Methodology · Statistics 2025-11-26 Hou Jian , Meng Tan , Tian Maozai

A multi-cube method is developed for solving systems of elliptic and hyperbolic partial differential equations numerically on manifolds with arbitrary spatial topologies. It is shown that any three-dimensional manifold can be represented as…

Computational Physics · Physics 2015-06-11 Lee Lindblom , Bela Szilagyi

We study how sampling geometry contributes to uncertainty in modeling spatial geophysical observations as sampled random fields characterized by stationary, isotropic, parametric covariance functions. We incorporate the signature of…

Methodology · Statistics 2026-04-03 Olivia L. Walbert , Frederik J. Simons , Arthur P. Guillaumin , Sofia C. Olhede

We study a homogenisation problem for problems of mixed type in the framework of evolutionary equations. The change of type is highly oscillatory. The numerical treatment is done by a discontinuous Galerkin method in time and a continuous…

Analysis of PDEs · Mathematics 2017-11-27 Sebastian Franz , Marcus Waurick

This paper considers nonconvex distributed constrained optimization over networks, modeled as directed (possibly time-varying) graphs. We introduce the first algorithmic framework for the minimization of the sum of a smooth nonconvex…

Optimization and Control · Mathematics 2018-09-05 Gesualdo Scutari , Ying Sun

This paper surveys the main results obtained during the period 1992-1999 on three aspects mentioned at the title. The first result is a new and general variational formula for the lower bound of spectral gap (i.e., the first non-trivial…

Probability · Mathematics 2007-05-23 Mu-Fa Chen

We introduce a fast algorithm for computing sparse Fourier transforms supported on smooth curves or surfaces. This problem appear naturally in several important problems in wave scattering and reflection seismology. The main observation is…

Numerical Analysis · Mathematics 2008-01-11 Lexing Ying

Level set method, introduced by Osher and Sethian [1], is a highly robust and accurate computational technique for tracking of moving interfaces in etching, deposition and photolithography processes. It originates from the idea to view the…

Plasma Physics · Physics 2007-05-23 Branislav Radenovic , S. J. Kim , J. K. Lee

We prove upper bounds for geodesic periods of automorphic forms over general rank one locally symmetric spaces. Such periods are integrals of automorphic forms restricted to special totally geodesic cycles of the ambient manifold and…

Number Theory · Mathematics 2019-01-10 Jan Möllers , Feng Su

We develop a framework for the study of the limiting behavior of multiple ergodic averages with commuting transformations when all iterates are given by the same sparse sequence; this enables us to partially resolve several longstanding…

Dynamical Systems · Mathematics 2025-11-20 Nikos Frantzikinakis , Borys Kuca

We continue our study [Domain Uncertainty Quantification in Computational Electromagnetics, JUQ (2020), 8:301--341] of the numerical approximation of time-harmonic electromagnetic fields for the Maxwell lossy cavity problem for uncertain…

Numerical Analysis · Mathematics 2022-12-15 Rubén Aylwin , Carlos Jerez-Hanckes , Christoph Schwab , Jakob Zech

This paper is a survey of some arithmetic applications of techniques in the geometry and ergodic theory of negatively curved Riemannian manifolds, focusing on the joint works of the authors. We describe Diophantine approximation results of…

Number Theory · Mathematics 2025-10-30 Jouni Parkkonen , Frédéric Paulin

Motivated by statistical inference problems in high-dimensional time series data analysis, we first derive non-asymptotic error bounds for Gaussian approximations of sums of high-dimensional dependent random vectors on hyper-rectangles,…

Statistics Theory · Mathematics 2024-06-05 Jinyuan Chang , Xiaohui Chen , Mingcong Wu

We introduce a new method for studying stochastic homogenization of elliptic equations in nondivergence form. The main application is an algebraic error estimate, asserting that deviations from the homogenized limit are at most proportional…

Analysis of PDEs · Mathematics 2019-12-10 Scott N. Armstrong , Charles K. Smart

An isogeometric approach for solving the Laplace-Beltrami equation on a two-dimensional manifold embedded in three-dimensional space using a Galerkin method based on Catmull-Clark subdivision surfaces is presented and assessed. The…

Numerical Analysis · Mathematics 2020-06-26 Zhaowei Liu , Andrew McBride , Prashant Saxena , Paul Steinmann

We consider covariance estimation in the multivariate generalized Gaussian distribution (MGGD) and elliptically symmetric (ES) distribution. The maximum likelihood optimization associated with this problem is non-convex, yet it has been…

Methodology · Statistics 2015-06-15 Teng Zhang , Ami Wiesel , Maria Sabrina Grec

We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional, stationary matrix-variate Gaussian time series. All past work on high-dimensional matrix graphical models assumes that independent…

Machine Learning · Statistics 2024-05-01 Jitendra K Tugnait
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