Cole Normale Suprieure

research

∙ 03/02/2020

Differential Privacy at Risk: Bridging Randomness and Privacy Budget

The calibration of noise for a privacy-preserving mechanism depends on t...

Ashish Dandekar, et al. ∙

research

∙ 05/22/2020

Reinforcement learning with human advice. A survey

In this paper, we provide an overview of the existing methods for integr...

Anis Najar, et al. ∙

research

∙ 06/20/2018

InfoCatVAE: Representation Learning with Categorical Variational Autoencoders

This paper describes InfoCatVAE, an extension of the variational autoenc...

Edouard Pineau, et al. ∙

research

∙ 06/24/2020

Distribution-Based Invariant Deep Networks for Learning Meta-Features

Recent advances in deep learning from probability distributions enable t...

Gwendoline de Bie, et al. ∙

research

∙ 10/11/2021

Phase Collapse in Neural Networks

Deep convolutional image classifiers progressively transform the spatial...

Florentin Guth, et al. ∙

research

∙ 05/13/2019

Universal Invariant and Equivariant Graph Neural Networks

Graph Neural Networks (GNN) come in many flavors, but should always be e...

Nicolas Keriven, et al. ∙

research

∙ 06/11/2019

Principled Training of Neural Networks with Direct Feedback Alignment

The backpropagation algorithm has long been the canonical training metho...

Julien Launay, et al. ∙

research

∙ 05/24/2019

Partially Encrypted Machine Learning using Functional Encryption

Machine learning on encrypted data has received a lot of attention thank...

Theo Ryffel, et al. ∙

research

∙ 10/08/2019

Deep Network classification by Scattering and Homotopy dictionary learning

We introduce a sparse scattering deep convolutional neural network, whic...

John Zarka, et al. ∙

research

∙ 11/08/2019

Degrees of freedom for off-the-grid sparse estimation

A central question in modern machine learning and imaging sciences is to...

Clarice Poon, et al. ∙

research

∙ 06/08/2020

ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

We propose ARIANN, a low-interaction framework to perform private traini...

Theo Ryffel, et al. ∙

research

∙ 11/16/2021

FedCostWAvg: A new averaging for better Federated Learning

We propose a simple new aggregation strategy for federated learning that...

Leon Mächler, et al. ∙

research

∙ 05/24/2018

Entropy and mutual information in models of deep neural networks

We examine a class of deep learning models with a tractable method to co...

Marylou Gabrié, et al. ∙

research

∙ 09/03/2021

Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations

Fast and accurate solutions of time-dependent partial differential equat...

Suprosanna, et al. ∙

research

∙ 06/11/2019

On the Universality of Noiseless Linear Estimation with Respect to the Measurement Matrix

In a noiseless linear estimation problem, one aims to reconstruct a vect...

Alia Abbara, et al. ∙

research

∙ 06/20/2019

Max-Plus Matching Pursuit for Deterministic Markov Decision Processes

We consider deterministic Markov decision processes (MDPs) and apply max...

Francis Bach, et al. ∙

research

∙ 07/08/2019

Asymptotic Bayes risk for Gaussian mixture in a semi-supervised setting

Semi-supervised learning (SSL) uses unlabeled data for training and has ...

Marc Lelarge, et al. ∙

research

∙ 05/13/2015

Brain Tumor Segmentation with Deep Neural Networks

In this paper, we present a fully automatic brain tumor segmentation met...

Mohammad Havaei, et al. ∙

research

∙ 01/03/2016

Wavelet Scattering on the Pitch Spiral

We present a new representation of harmonic sounds that linearizes the d...

Vincent Lostanlen, et al. ∙

research

∙ 12/09/2016

Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity

In machine learning, error back-propagation in multi-layer neural networ...

Thomas Mesnard, et al. ∙

research

∙ 06/08/2016

Improving Recurrent Neural Networks For Sequence Labelling

In this paper we study different types of Recurrent Neural Networks (RNN...

Marco Dinarelli, et al. ∙

research

∙ 07/05/2017

A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series

Sleep stage classification constitutes an important preliminary exam in ...

Stanislas Chambon, et al. ∙

research

∙ 06/22/2017

Compressive Statistical Learning with Random Feature Moments

We describe a general framework --compressive statistical learning-- for...

Rémi Gribonval, et al. ∙

research

∙ 06/06/2017

GAN and VAE from an Optimal Transport Point of View

This short article revisits some of the ideas introduced in arXiv:1701.0...

Aude Genevay, et al. ∙

research

∙ 11/26/2016

Multiwinner Approval Rules as Apportionment Methods

We establish a link between multiwinner elections and apportionment prob...

Markus Brill, et al. ∙

research

∙ 02/21/2017

Stochastic Composite Least-Squares Regression with convergence rate O(1/n)

We consider the minimization of composite objective functions composed o...

Nicolas Flammarion, et al. ∙

research

∙ 03/27/2017

Scaling the Scattering Transform: Deep Hybrid Networks

We use the scattering network as a generic and fixed ini-tialization of ...

Edouard Oyallon, et al. ∙

research

∙ 03/21/2017

Proposal Flow: Semantic Correspondences from Object Proposals

Finding image correspondences remains a challenging problem in the prese...

Bumsub Ham, et al. ∙

research

∙ 03/06/2017

Building a Regular Decision Boundary with Deep Networks

In this work, we build a generic architecture of Convolutional Neural Ne...

Edouard Oyallon, et al. ∙

research

∙ 09/08/2016

Non-Backtracking Spectrum of Degree-Corrected Stochastic Block Models

Motivated by community detection, we characterise the spectrum of the no...

Lennart Gulikers, et al. ∙

research

∙ 08/29/2016

Robust Discriminative Clustering with Sparse Regularizers

Clustering high-dimensional data often requires some form of dimensional...

Nicolas Flammarion, et al. ∙

research

∙ 12/21/2017

Truncated Variational Sampling for "Black Box" Optimization of Generative Models

We investigate the optimization of two generative models with binary hid...

Jörg Lücke, et al. ∙

research

∙ 07/08/2016

Optimal Rates of Statistical Seriation

Given a matrix the seriation problem consists in permuting its rows in s...

Nicolas Flammarion, et al. ∙

research

∙ 04/07/2016

Online Optimization of Smoothed Piecewise Constant Functions

We study online optimization of smoothed piecewise constant functions ov...

Vincent Cohen-Addad, et al. ∙

research

∙ 06/20/2014

Spectral Ranking using Seriation

We describe a seriation algorithm for ranking a set of items given pairw...

Fajwel Fogel, et al. ∙

research

∙ 03/08/2016

Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling

We study parameter inference in large-scale latent variable models. We f...

Christophe Dupuy, et al. ∙

research

∙ 02/17/2016

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

We consider the optimization of a quadratic objective function whose gra...

Aymeric Dieuleveut, et al. ∙

research

∙ 08/21/2016

Congruences and Concurrent Lines in Multi-View Geometry

We present a new framework for multi-view geometry in computer vision. A...

Jean Ponce, et al. ∙

research

∙ 04/13/2015

Streaming, Memory Limited Matrix Completion with Noise

In this paper, we consider the streaming memory-limited matrix completio...

Se-Young Yun, et al. ∙

research

∙ 04/07/2015

From Averaging to Acceleration, There is Only a Step-size

We show that accelerated gradient descent, averaged gradient descent and...

Nicolas Flammarion, et al. ∙

research

∙ 02/24/2015

On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions

We show that kernel-based quadrature rules for computing integrals can b...

Francis Bach, et al. ∙

research

∙ 02/11/2015

Combinatorial Bandits Revisited

This paper investigates stochastic and adversarial combinatorial multi-a...

Richard Combes, et al. ∙

research

∙ 02/11/2015

Reconstruction in the Labeled Stochastic Block Model

The labeled stochastic block model is a random graph model representing ...

Marc Lelarge, et al. ∙

research

∙ 07/19/2014

Sparse and spurious: dictionary learning with noise and outliers

A popular approach within the signal processing and machine learning com...

Rémi Gribonval, et al. ∙

research

∙ 06/26/2014

Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results

The classical setting of community detection consists of networks exhibi...

Jiaming Xu, et al. ∙

research

∙ 03/20/2014

On The Sample Complexity of Sparse Dictionary Learning

In the synthesis model signals are represented as a sparse combinations ...

Matthias Seibert, et al. ∙

research

∙ 09/10/2013

Minimizing Finite Sums with the Stochastic Average Gradient

We propose the stochastic average gradient (SAG) method for optimizing t...

Mark Schmidt, et al. ∙

research

∙ 07/13/2013

MCMC Learning

The theory of learning under the uniform distribution is rich and deep, ...

Varun Kanade, et al. ∙

research

∙ 06/10/2013

Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)

We consider the stochastic approximation problem where a convex function...

Francis Bach, et al. ∙

research

∙ 06/01/2015

A Riemannian low-rank method for optimization over semidefinite matrices with block-diagonal constraints

We propose a new algorithm to solve optimization problems of the form f...

Nicolas Boumal, et al. ∙

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