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Publications associées (103)

What graph neural networks cannot learn: depth vs width

This paper studies the expressive power of graph neural networks falling within the message-passing framework (GNNmp). Two results are presented. First, GNNmp are shown to be Turing universal under sufficient conditions on their depth, width, node attribut ...

2020

Exploring Blink-Rate Behaviors for Cybersickness Detection in VR

Ronan Boulic, Nana Tian

Cybersickness continues to be one of the main barriers for mainstream adoption of Virtual Reality (VR). Despite the wealth of research available on this topic, it is still an unsolved problem. This paper explores the potential influence of cybersickness ov ...

IEEE2020

MATHICSE Technical Report: Constraint-Aware Neural Networks for Riemann Problems

Jan Sickmann Hesthaven, Deep Ray

Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical constraints, such as mass or energy conservation, must be satised to obtain ...

MATHICSE2019

Efficient Greedy Coordinate Descent for Composite Problems

Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova, Sai Praneeth Reddy Karimireddy

Coordinate descent with random coordinate selection is the current state of the art for many large scale optimization problems. However, greedy selection of the steepest coordinate on smooth problems can yield convergence rates independent of the dimension ...

MICROTOME PUBLISHING2019

Efficient Greedy Coordinate Descent for Composite Problems

Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova, Sai Praneeth Reddy Karimireddy

2019

Education in Process Systems Engineering: Why it matters more than ever and how it can be structured

Dominique Bonvin

This position paper is an outcome of discussions that took place at the third FIPSE Symposium in Rhodes, Greece, between June 20-22, 2016 (http://fi-in-pse.org). The FIPSE objective is to discuss open research challenges in topics of Process Systems Engine ...

PERGAMON-ELSEVIER SCIENCE LTD2019

Concentration phenomena for a fractional Choquard equation with magnetic field

We consider the following nonlinear fractional Choquard equation epsilon(2s) (-Delta)(A/epsilon)(s) u + V(x)u = epsilon(mu-N) (1/vertical bar x vertical bar(mu) * F vertical bar mu vertical bar(2))) f(vertical bar u vertical bar(2))u in R-N, where epsilon ...

2019

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials

Damian Mateusz Straszak

Optimization is a fundamental tool in modern science. Numerous important tasks in biology, economy, physics and computer science can be cast as optimization problems. Consider the example of machine learning: recent advances have shown that even the most s ...

EPFL2018

From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming

Daniel Kuhn, John Lygeros, Peyman Mohajerin Esfahani, Tobias Sutter

We consider linear programming (LP) problems in infinite dimensional spaces that are in general computationally intractable. Under suitable assumptions, we develop an approximation bridge from the infinite-dimensional LP to tractable finite convex programs ...

2018

State Machine Replication is More Expensive than Consensus

Rachid Guerraoui, Dragos-Adrian Seredinschi, Karolos Antoniadis

Consensus and State Machine Replication (SMR) are generally considered to be equivalent problems. In certain system models, indeed, the two problems are computationally equivalent: any solution to the former problem leads to a solution to the latter, and v ...

2018

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