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Concept

Discontinuous linear map

Publications associées (28)

Unified theory of atom-centered representations and message-passing machine-learning schemes

Michele Ceriotti, Jigyasa Nigam, Guillaume André Jean Fraux, Sergey Pozdnyakov

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-cen ...

AIP Publishing2022

A Shadow Perspective on Equivariant Hochschild Homologies

Kathryn Hess Bellwald

Shadows for bicategories, defined by Ponto, provide a useful framework that generalizes classical and topological Hochschild homology. In this paper, we define Hochschild-type invariants for monoids in a symmetric monoidal, simplicial model category V, as ...

OXFORD UNIV PRESS2022

SPAHM: the spectrum of approximated Hamiltonian matrices representations

Anne-Clémence Corminboeuf, Alberto Fabrizio, Ksenia Briling

Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all r ...

2022

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

Mireille El Gheche

Proximal splitting methods are standard tools for nonsmooth optimization. While primal-dual methods have become very popular in the last decade for their flexibility, primal methods may still be preferred for two reasons: acceleration schemes are more effe ...

IEEE2021

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Michele Ceriotti, Michael John Willatt

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear fun ...

Springer International Publishing2020

Streaming Principal Component Analysis From Incomplete Data

Armin Eftekhari

Linear subspace models are pervasive in computational sciences and particularly used for large datasets which are often incomplete due to privacy issues or sampling constraints. Therefore, a critical problem is developing an efficient algorithm for detecti ...

Microtome Publishing2019

System for planning and/or providing neuromodulation, especially neurostimulation

Grégoire Courtine, Marco Capogrosso, Fabien Bertrand Paul Wagner, Vincent Delattre, Karen Minassian, Miroslav Caban

The present invention relates system for planning and/or providing neuromodulation (10), especially neurostimulation, comprising - a neurostimulator (24) comprising a least one electrode (22), - functional mapping module (38) configured and arranged such t ...

2019

A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms

Mikhail Kapralov, Amir Zandieh

Reconstructing continuous signals based on a small number of discrete samples is a fundamental problem across science and engineering. We are often interested in signals with "simple" Fourier structure - e.g., those involving frequencies within a bounded r ...

ASSOC COMPUTING MACHINERY2019

Measurement of Displacement and Its Derivatives from a Phase Fringe Pattern

Pramod Rastogi, Rishikesh Dilip Kulkarni

Noting the fact that the phase and phase derivatives carry the information on the mechanical deformation in optical interferometric measurements techniques, we propose a noise robust fringe analysis technique for the simultaneous estimation of unwrapped ph ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Near-Optimal Finite-Length Scaling for Polar Codes Over Large Alphabets

Rüdiger Urbanke

For any prime power q, Mori and Tanaka introduced a family of q-ary polar codes based on the q x q Reed-Solomon polarization kernels. For transmission over a q-ary erasure channel, they also derived a closed-form recursion for the erasure probability of ea ...

2019

Stochastic Three-Composite Convex Minimization with a Linear Operator

Volkan Cevher

We develop a primal-dual convex minimization framework to solve a class of stochastic convex three-composite problem with a linear operator. We consider the cases where the problem is both convex and strongly convex and analyze the convergence of the propo ...

JMLR: W&CP2018

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

Michaël Unser, Julien René Fageot, Anaïs Laure Marie-Thérèse Badoual

This paper deals with the resolution of inverse problems in a periodic setting or, in other terms, the reconstruction of periodic continuous-domain signals from their noisy measurements. We focus on two reconstruction paradigms: variational and statistical ...

2018

Distributed Signal Processing via Chebyshev Polynomial Approximation

Pascal Frossard, Pierre Vandergheynst, Daniel Kressner, David Shuman

Unions of graph multiplier operators are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators. The proposed method features approximations of ...

2018

Methodology And Convergence Rates For Functional Time Series Regression

Victor Panaretos, Tung Huy Pham

The functional linear model extends the notion of linear regression to the case where the response and covariates are iid elements of an infinite-dimensional Hilbert space. The unknown to be estimated is a Hilbert-Schmidt operator, whose inverse is by defi ...

STATISTICA SINICA2018

On the linear convergence of the stochastic gradient method with constant step-size

Volkan Cevher, Cong Bang Vu

The strong growth condition (SGC) is known to be a sufficient condition for linear convergence of the stochastic gradient method using a constant step-size γ (SGM-CS). In this paper, we provide a necessary condition, for the linear convergence of SGM-CS, t ...

2018

Smoothing technique for nonsmooth composite minimization with linear operator

Volkan Cevher, Van Quang Nguyen

We introduce and analyze an algorithm for the minimization of convex functions that are the sum of differentiable terms and proximable terms composed with linear operators. The method builds upon the recently developed smoothed gap technique. In addition t ...

2017

Towards Stationary Time-Vertex Signal Processing

Pierre Vandergheynst, Nathanaël Perraudin, Andreas Loukas, Francesco Grassi

Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks. Yet, though these systems are often dynamic, state-of-the-art methods ...

Ieee2017

Preconditioned Low-Rank Riemannian Optimization For Linear Systems With Tensor Product Structure

Daniel Kressner, Michael Maximilian Steinlechner

The numerical solution of partial differential equations on high-dimensional domains gives rise to computationally challenging linear systems. When using standard discretization techniques, the size of the linear system grows exponentially with the number ...

Siam Publications2016

Structure Tensor Total Variation

Michaël Unser, Stamatios Lefkimmiatis

We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure ...

SIAM2015

Phase estimation from noisy phase fringe patterns using linearly independent basis functions

Pramod Rastogi, Rishikesh Dilip Kulkarni

A novel technique is proposed for obtaining unwrapped phase estimation from a highly noisy exponential phase field. In this technique, the interference phase is represented as a linear combination of linearly independent and pre-defined basis functions alo ...

Iop Publishing Ltd2015