Cole Normale Suprieure

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The École Normale Supérieure (ENS) is a higher institution for pre-doctoral and graduate studies and a high place for French research. It hosts fourteen teaching and research departments, covering the bulk of literary and scientific disciplines.

Differential Privacy at Risk: Bridging Randomness and Privacy Budget

The calibration of noise for a privacy-preserving mechanism depends on t...
03/02/2020 ∙ by Ashish Dandekar, et al. ∙ 37 ∙ share

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Reinforcement learning with human advice. A survey

In this paper, we provide an overview of the existing methods for integr...
05/22/2020 ∙ by Anis Najar, et al. ∙ 29 ∙ share

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InfoCatVAE: Representation Learning with Categorical Variational Autoencoders

This paper describes InfoCatVAE, an extension of the variational autoenc...
06/20/2018 ∙ by Edouard Pineau, et al. ∙ 24 ∙ share

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Distribution-Based Invariant Deep Networks for Learning Meta-Features

Recent advances in deep learning from probability distributions enable t...
06/24/2020 ∙ by Gwendoline de Bie, et al. ∙ 15 ∙ share

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Universal Invariant and Equivariant Graph Neural Networks

Graph Neural Networks (GNN) come in many flavors, but should always be e...
05/13/2019 ∙ by Nicolas Keriven, et al. ∙ 13 ∙ share

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Principled Training of Neural Networks with Direct Feedback Alignment

The backpropagation algorithm has long been the canonical training metho...
06/11/2019 ∙ by Julien Launay, et al. ∙ 13 ∙ share

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Partially Encrypted Machine Learning using Functional Encryption

Machine learning on encrypted data has received a lot of attention thank...
05/24/2019 ∙ by Theo Ryffel, et al. ∙ 12 ∙ share

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Deep Network classification by Scattering and Homotopy dictionary learning

We introduce a sparse scattering deep convolutional neural network, whic...
10/08/2019 ∙ by John Zarka, et al. ∙ 9 ∙ share

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Degrees of freedom for off-the-grid sparse estimation

A central question in modern machine learning and imaging sciences is to...
11/08/2019 ∙ by Clarice Poon, et al. ∙ 6 ∙ share

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

We propose ARIANN, a low-interaction framework to perform private traini...
06/08/2020 ∙ by Theo Ryffel, et al. ∙ 6 ∙ share

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Entropy and mutual information in models of deep neural networks

We examine a class of deep learning models with a tractable method to co...
05/24/2018 ∙ by Marylou Gabrié, et al. ∙ 4 ∙ share

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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...
06/11/2019 ∙ by Alia Abbara, et al. ∙ 1 ∙ share

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Max-Plus Matching Pursuit for Deterministic Markov Decision Processes

We consider deterministic Markov decision processes (MDPs) and apply max...
06/20/2019 ∙ by Francis Bach, et al. ∙ 1 ∙ share

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

Semi-supervised learning (SSL) uses unlabeled data for training and has ...
07/08/2019 ∙ by Marc Lelarge, et al. ∙ 1 ∙ share

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Brain Tumor Segmentation with Deep Neural Networks

In this paper, we present a fully automatic brain tumor segmentation met...
05/13/2015 ∙ by Mohammad Havaei, et al. ∙ 0 ∙ share

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Wavelet Scattering on the Pitch Spiral

We present a new representation of harmonic sounds that linearizes the d...
01/03/2016 ∙ by Vincent Lostanlen, et al. ∙ 0 ∙ share

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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...
12/09/2016 ∙ by Thomas Mesnard, et al. ∙ 0 ∙ share

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Improving Recurrent Neural Networks For Sequence Labelling

In this paper we study different types of Recurrent Neural Networks (RNN...
06/08/2016 ∙ by Marco Dinarelli, et al. ∙ 0 ∙ share

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

Sleep stage classification constitutes an important preliminary exam in ...
07/05/2017 ∙ by Stanislas Chambon, et al. ∙ 0 ∙ share

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Compressive Statistical Learning with Random Feature Moments

We describe a general framework --compressive statistical learning-- for...
06/22/2017 ∙ by Rémi Gribonval, et al. ∙ 0 ∙ share

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GAN and VAE from an Optimal Transport Point of View

This short article revisits some of the ideas introduced in arXiv:1701.0...
06/06/2017 ∙ by Aude Genevay, et al. ∙ 0 ∙ share

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Multiwinner Approval Rules as Apportionment Methods

We establish a link between multiwinner elections and apportionment prob...
11/26/2016 ∙ by Markus Brill, et al. ∙ 0 ∙ share

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

We consider the minimization of composite objective functions composed o...
02/21/2017 ∙ by Nicolas Flammarion, et al. ∙ 0 ∙ share

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Scaling the Scattering Transform: Deep Hybrid Networks

We use the scattering network as a generic and fixed ini-tialization of ...
03/27/2017 ∙ by Edouard Oyallon, et al. ∙ 0 ∙ share

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Proposal Flow: Semantic Correspondences from Object Proposals

Finding image correspondences remains a challenging problem in the prese...
03/21/2017 ∙ by Bumsub Ham, et al. ∙ 0 ∙ share

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Building a Regular Decision Boundary with Deep Networks

In this work, we build a generic architecture of Convolutional Neural Ne...
03/06/2017 ∙ by Edouard Oyallon, et al. ∙ 0 ∙ share

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Non-Backtracking Spectrum of Degree-Corrected Stochastic Block Models

Motivated by community detection, we characterise the spectrum of the no...
09/08/2016 ∙ by Lennart Gulikers, et al. ∙ 0 ∙ share

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Robust Discriminative Clustering with Sparse Regularizers

Clustering high-dimensional data often requires some form of dimensional...
08/29/2016 ∙ by Nicolas Flammarion, et al. ∙ 0 ∙ share

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

We investigate the optimization of two generative models with binary hid...
12/21/2017 ∙ by Jörg Lücke, et al. ∙ 0 ∙ share

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Optimal Rates of Statistical Seriation

Given a matrix the seriation problem consists in permuting its rows in s...
07/08/2016 ∙ by Nicolas Flammarion, et al. ∙ 0 ∙ share

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Online Optimization of Smoothed Piecewise Constant Functions

We study online optimization of smoothed piecewise constant functions ov...
04/07/2016 ∙ by Vincent Cohen-Addad, et al. ∙ 0 ∙ share

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Spectral Ranking using Seriation

We describe a seriation algorithm for ranking a set of items given pairw...
06/20/2014 ∙ by Fajwel Fogel, et al. ∙ 0 ∙ share

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

We study parameter inference in large-scale latent variable models. We f...
03/08/2016 ∙ by Christophe Dupuy, et al. ∙ 0 ∙ share

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

We consider the optimization of a quadratic objective function whose gra...
02/17/2016 ∙ by Aymeric Dieuleveut, et al. ∙ 0 ∙ share

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Congruences and Concurrent Lines in Multi-View Geometry

We present a new framework for multi-view geometry in computer vision. A...
08/21/2016 ∙ by Jean Ponce, et al. ∙ 0 ∙ share

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Streaming, Memory Limited Matrix Completion with Noise

In this paper, we consider the streaming memory-limited matrix completio...
04/13/2015 ∙ by Se-Young Yun, et al. ∙ 0 ∙ share

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

We show that accelerated gradient descent, averaged gradient descent and...
04/07/2015 ∙ by Nicolas Flammarion, et al. ∙ 0 ∙ share

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On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions

We show that kernel-based quadrature rules for computing integrals can b...
02/24/2015 ∙ by Francis Bach, et al. ∙ 0 ∙ share

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Combinatorial Bandits Revisited

This paper investigates stochastic and adversarial combinatorial multi-a...
02/11/2015 ∙ by Richard Combes, et al. ∙ 0 ∙ share

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Reconstruction in the Labeled Stochastic Block Model

The labeled stochastic block model is a random graph model representing ...
02/11/2015 ∙ by Marc Lelarge, et al. ∙ 0 ∙ share

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Sparse and spurious: dictionary learning with noise and outliers

A popular approach within the signal processing and machine learning com...
07/19/2014 ∙ by Rémi Gribonval, et al. ∙ 0 ∙ share

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

The classical setting of community detection consists of networks exhibi...
06/26/2014 ∙ by Jiaming Xu, et al. ∙ 0 ∙ share

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On The Sample Complexity of Sparse Dictionary Learning

In the synthesis model signals are represented as a sparse combinations ...
03/20/2014 ∙ by Matthias Seibert, et al. ∙ 0 ∙ share

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Minimizing Finite Sums with the Stochastic Average Gradient

We propose the stochastic average gradient (SAG) method for optimizing t...
09/10/2013 ∙ by Mark Schmidt, et al. ∙ 0 ∙ share

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MCMC Learning

The theory of learning under the uniform distribution is rich and deep, ...
07/13/2013 ∙ by Varun Kanade, et al. ∙ 0 ∙ share

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

We consider the stochastic approximation problem where a convex function...
06/10/2013 ∙ by Francis Bach, et al. ∙ 0 ∙ share

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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...
06/01/2015 ∙ by Nicolas Boumal, et al. ∙ 0 ∙ share

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Large-Margin Metric Learning for Partitioning Problems

In this paper, we consider unsupervised partitioning problems, such as c...
03/06/2013 ∙ by Rémi Lajugie, et al. ∙ 0 ∙ share

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Duality between subgradient and conditional gradient methods

Given a convex optimization problem and its dual, there are many possibl...
11/27/2012 ∙ by Francis Bach, et al. ∙ 0 ∙ share

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Forecasting electricity consumption by aggregating specialized experts

We consider the setting of sequential prediction of arbitrary sequences ...
07/09/2012 ∙ by Marie Devaine, et al. ∙ 0 ∙ share

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