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Publications associées (37)

SPAHM: the spectrum of approximated Hamiltonian matrices representations

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, Giovanni Chierchia

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 PUBL2019

System for planning and/or providing neuromodulation, especially neurostimulation

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

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, Henry Pfister

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 Learning Under Random Reshuffling With Constant Step-Sizes

Ali H. Sayed, Stefan Vlaski, Bicheng Ying, Kun Yuan

In empirical risk optimization, it has been observed that stochastic gradient implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data uniformly. Recent works have pursued ju ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

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

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