Inria

∙ Rocquencourt , France ∙ 0 followers

It was created under the name Institut de recherche en informatique et en automatique in 1967 at Rocquencourt near Paris, part of Plan Calcul.

Featured Researchers

Francis Bach
118 publications
Ji Liu
79 publications
Cordelia Schmid
77 publications
Remi Munos
60 publications
Julien Mairal
51 publications
Alexandre Gramfort
46 publications
Mohammad Ghavamzadeh
45 publications
Gaël Varoquaux
39 publications
Pierre-Yves Oudeyer
38 publications
Radu Horaud
38 publications
Ivan Laptev
37 publications

Convolutional Kernel Networks for Graph-Structured Data

We introduce a family of multilayer graph kernels and establish new link...
03/11/2020 ∙ by Dexiong Chen, et al. ∙ 46 ∙ share

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Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more

Cyanure is an open-source C++ software package with a Python interface. ...
12/17/2019 ∙ by Julien Mairal, et al. ∙ 39 ∙ share

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End-to-End Learning of Visual Representations from Uncurated Instructional Videos

Annotating videos is cumbersome, expensive and not scalable. Yet, many s...
12/13/2019 ∙ by Antoine Miech, et al. ∙ 35 ∙ share

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Kernel-Based Ensemble Learning in Python

We propose a new supervised learning algorithm, for classification and r...
12/17/2019 ∙ by Benjamin Guedj, et al. ∙ 35 ∙ share

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Automatic Curriculum Learning For Deep RL: A Short Survey

Automatic Curriculum Learning (ACL) has become a cornerstone of recent s...
03/10/2020 ∙ by Rémy Portelas, et al. ∙ 30 ∙ share

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PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems

Interpretable explanations for recommender systems and other machine lea...
11/19/2019 ∙ by Azin Ghazimatin, et al. ∙ 28 ∙ share

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A sub-Riemannian model of the visual cortex with frequency and phase

In this paper we present a novel model of the primary visual cortex (V1)...
10/11/2019 ∙ by E. Baspinar, et al. ∙ 26 ∙ share

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On Fast Leverage Score Sampling and Optimal Learning

Leverage score sampling provides an appealing way to perform approximate...
10/31/2018 ∙ by Alessandro Rudi, et al. ∙ 24 ∙ share

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DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images

The domain adaptation of satellite images has recently gained an increas...
05/13/2020 ∙ by Onur Tasar, et al. ∙ 23 ∙ share

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Automated Machine Learning with Monte-Carlo Tree Search (Extended Version)

The AutoML task consists of selecting the proper algorithm in a machine ...
06/01/2019 ∙ by Herilalaina Rakotoarison, et al. ∙ 22 ∙ share

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Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow

We propose a method to classify cardiac pathology based on a novel appro...
11/08/2018 ∙ by Qiao Zheng, et al. ∙ 18 ∙ share

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A Generative 3D Facial Model by Adversarial Training

We consider data-driven generative models for the 3D face, and focus in ...
02/10/2019 ∙ by Victoria Fernandez Abrevaya, et al. ∙ 18 ∙ share

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Whittle index based Q-learning for restless bandits with average reward

A novel reinforcement learning algorithm is introduced for multiarmed re...
04/29/2020 ∙ by Konstantin Avrachenkov, et al. ∙ 18 ∙ share

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High-Dimensional Control Using Generalized Auxiliary Tasks

A long-standing challenge in reinforcement learning is the design of fun...
09/26/2019 ∙ by Yannis Flet-Berliac, et al. ∙ 17 ∙ share

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Multi-Domain Adversarial Learning

Multi-domain learning (MDL) aims at obtaining a model with minimal avera...
03/21/2019 ∙ by Alice Schoenauer-Sebag, et al. ∙ 14 ∙ share

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Stochastic Optimization for Regularized Wasserstein Estimators

Optimal transport is a foundational problem in optimization, that allows...
02/20/2020 ∙ by Marin Ballu, et al. ∙ 13 ∙ share

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Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning

Autonomous reinforcement learning agents, like children, do not have acc...
11/08/2019 ∙ by Nicolas Lair, et al. ∙ 12 ∙ share

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Elastic registration based on compliance analysis and biomechanical graph matching

An automatic elastic registration method suited for vascularized organs ...
12/13/2019 ∙ by Jaime Garcia Guevara, et al. ∙ 12 ∙ share

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Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors

Meta-learning algorithms can accelerate the model-based reinforcement le...
03/10/2020 ∙ by Rituraj Kaushik, et al. ∙ 12 ∙ share

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Selecting Relevant Features from a Universal Representation for Few-shot Classification

Popular approaches for few-shot classification consist of first learning...
03/20/2020 ∙ by Nikita Dvornik, et al. ∙ 12 ∙ share

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StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization

Domain adaptation for semantic segmentation has recently been actively s...
04/14/2020 ∙ by Onur Tasar, et al. ∙ 11 ∙ share

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Designing and Learning Trainable Priors with Non-Cooperative Games

We introduce a general framework for designing and learning neural netwo...
06/26/2020 ∙ by Bruno Lecouat, et al. ∙ 11 ∙ share

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Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards

The most data-efficient algorithms for reinforcement learning in robotic...
06/25/2018 ∙ by Rituraj Kaushik, et al. ∙ 10 ∙ share

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Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling

In this paper, we propose and study a Nyström based approach to efficien...
07/11/2019 ∙ by Nicholas Sterge, et al. ∙ 10 ∙ share

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Recurrent Kernel Networks

Substring kernels are classical tools for representing biological sequen...
06/07/2019 ∙ by Dexiong Chen, et al. ∙ 9 ∙ share

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On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport

Many tasks in machine learning and signal processing can be solved by mi...
05/24/2018 ∙ by Lenaïc Chizat, et al. ∙ 8 ∙ share

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Stable safe screening and structured dictionaries for faster ℓ_1 regularization

In this paper, we propose a way to combine two acceleration techniques f...
12/17/2018 ∙ by Cassio Dantas, et al. ∙ 8 ∙ share

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Gaussian Graphical Model exploration and selection in high dimension low sample size setting

Gaussian Graphical Models (GGM) are often used to describe the condition...
03/11/2020 ∙ by Thomas Lartigue, et al. ∙ 8 ∙ share

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Deep Sets for Generalization in RL

This paper investigates the idea of encoding object-centered representat...
03/20/2020 ∙ by Tristan Karch, et al. ∙ 8 ∙ share

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Progressive growing of self-organized hierarchical representations for exploration

Designing agent that can autonomously discover and learn a diversity of ...
05/13/2020 ∙ by Mayalen Etcheverry, et al. ∙ 8 ∙ share

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Efficient Topological Layer based on Persistent Landscapes

We propose a novel topological layer for general deep learning models ba...
02/07/2020 ∙ by Kwangho Kim, et al. ∙ 7 ∙ share

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The GeoLifeCLEF 2020 Dataset

Understanding the geographic distribution of species is a key concern in...
04/08/2020 ∙ by Elijah Cole, et al. ∙ 7 ∙ share

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Perspective-Aware CNN For Crowd Counting

Crowd counting is the task of estimating pedestrian numbers in crowd ima...
07/05/2018 ∙ by Miaojing Shi, et al. ∙ 6 ∙ share

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Context-adaptive neural network based prediction for image compression

This paper describes a set of neural network architectures, called Predi...
07/17/2018 ∙ by Thierry Dumas, et al. ∙ 6 ∙ share

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Learning to Localize and Align Fine-Grained Actions to Sparse Instructions

Automatic generation of textual video descriptions that are time-aligned...
09/22/2018 ∙ by Meera Hahn, et al. ∙ 6 ∙ share

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Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior

We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF...
10/30/2018 ∙ by Gaël Letarte, et al. ∙ 6 ∙ share

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SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action Recognition

Due to the availability of large-scale skeleton datasets, 3D human actio...
07/30/2019 ∙ by Carlos Caetano, et al. ∙ 6 ∙ share

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Online k-means Clustering

We study the problem of online clustering where a clustering algorithm h...
09/15/2019 ∙ by Vincent Cohen-Addad, et al. ∙ 6 ∙ share

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Fast shared response model for fMRI data

The shared response model provides a simple but effective framework toan...
09/27/2019 ∙ by Hugo Richard, et al. ∙ 6 ∙ share

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Automating Representation Discovery with MAP-Elites

The way solutions are represented, or encoded, is usually the result of ...
03/09/2020 ∙ by Adam Gaier, et al. ∙ 6 ∙ share

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Generalized Penalty for Circular Coordinate Representation

Topological Data Analysis (TDA) provides novel approaches that allow us ...
06/03/2020 ∙ by Hengrui Luo, et al. ∙ 6 ∙ share

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VPN: Learning Video-Pose Embedding for Activities of Daily Living

In this paper, we focus on the spatio-temporal aspect of recognizing Act...
07/06/2020 ∙ by Srijan Das, et al. ∙ 6 ∙ share

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3D Surface Reconstruction from Voxel-based Lidar Data

To achieve fully autonomous navigation, vehicles need to compute an accu...
06/25/2019 ∙ by Luis Roldao, et al. ∙ 5 ∙ share

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Learning a Behavioral Repertoire from Demonstrations

Imitation Learning (IL) is a machine learning approach to learn a policy...
07/05/2019 ∙ by Niels Justesen, et al. ∙ 5 ∙ share

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Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints

In the last years, the computer vision research community has studied on...
09/11/2019 ∙ by Carlos Caetano, et al. ∙ 5 ∙ share

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Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index

In this paper, we make an experimental comparison of semi-parametric (Co...
03/13/2020 ∙ by Camila Fernandez, et al. ∙ 5 ∙ share

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Modeling Spatio-Temporal Human Track Structure for Action Localization

This paper addresses spatio-temporal localization of human actions in vi...
06/28/2018 ∙ by Guilhem Chéron, et al. ∙ 4 ∙ share

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Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation

Convolutional neural networks are designed for dense data, but vision da...
08/02/2018 ∙ by Maximilian Jaritz, et al. ∙ 4 ∙ share

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Massively scalable Sinkhorn distances via the Nyström method

The Sinkhorn distance, a variant of the Wasserstein distance with entrop...
12/12/2018 ∙ by Jason Altschuler, et al. ∙ 4 ∙ share

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A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise

We consider variational inequalities coming from monotone operators, a s...
02/05/2019 ∙ by Francis Bach, et al. ∙ 4 ∙ share

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