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Publications associées (30)

Fourier could be a data scientist: From graph Fourier transform to signal processing on graphs

Pierre Vandergheynst, Benjamin Ricaud, Nicolas Tremblay

The legacy of Joseph Fourier in science is vast, especially thanks to the essential tool that the Fourier transform is. The flexibility of this analysis, its computational efficiency and the physical interpretation it offers makes it a cornerstone in many ...

ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER2019

Asynchronous Simulation of Neuronal Activity

Bruno Ricardo Da Cunha Magalhães

Simulations of the electrical activity of networks of morphologically-detailed neuron models allow for a better understanding of the brain. Short time to solution is critical in order to study long biological processes such as synaptic plasticity and learn ...

EPFL2019

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

Deterministic Algorithms for Submodular Maximization Problems

Moran Feldman

Randomization is a fundamental tool used in many theoretical and practical areas of computer science. We study here the role of randomization in the area of submodular function maximization. In this area, most algorithms are randomized, and in almost all c ...

ASSOC COMPUTING MACHINERY2018

Towards a scalable quantum computer

Edoardo Charbon, Fabio Sebastiano

A quantum machine may solve some complex problems that are intractable for even the most powerful classical computers. By exploiting quantum superposition and entanglement phenomena, quantum algorithms can achieve from polynomial to exponential speed up wh ...

2018

Deep Learning Works in Practice. But Does it Work in Theory?

Rachid Guerraoui, Le Nguyen Hoang

Deep learning relies on a very specific kind of neural networks: those superposing several neural layers. In the last few years, deep learning achieved major breakthroughs in many tasks such as image analysis, speech recognition, natural language processin ...

2018

How Computer Science Risks to Lose its Innocence, and Should Attempt to Take Responsibility

Karl Aberer

Computer science is playing a driving role in transforming today's society through information technology. In this transformation we observe power shifts increasingly strengthening centralised organisations, which are negatively perceived by many people. W ...

Ieee Computer Soc2017

Rake, Peel, Sketch : The Signal Processing Pipeline Revisited

Robin Scheibler

The prototypical signal processing pipeline can be divided into four blocks. Representation of the signal in a basis suitable for processing. Enhancement of the meaningful part of the signal and noise reduction. Estimation of important statistical properti ...

EPFL2017

Bringing robotics into formal education using the Thymio open source hardware robot

Francesco Mondada, Michael Bonani, Léa Marie Pereyre, Fanny Riedo, Stéphane Magnenat, Philippe Rétornaz, Manon Aline Briod

Mobile robots are valuable tools for education because of both the enthusiasm they raise and the multidisciplinary nature of robotics technology. Mobile robots give access to a wide range of fields, such as complex mechanics, sensors, wireless transmission ...

Institute of Electrical and Electronics Engineers2017

Subdeterminant Maximization via Nonconvex Relaxations and Anti-Concentration

Nisheeth Vishnoi, Javad Ebrahimi Boroojeni, Damian Mateusz Straszak

Several fundamental problems that arise in optimization and computer science can be cast as follows: Given vectors v(1), ..., v(m) is an element of R-d and a constraint family B subset of 2([m]), find a set S. B that maximizes the squared volume of the sim ...

Ieee2017

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