b'David Filliat'

research

∙ 06/14/2023

VIBR: Learning View-Invariant Value Functions for Robust Visual Control

End-to-end reinforcement learning on images showed significant progress ...

0 Tom Dupuis, et al. ∙

research

∙ 07/20/2022

Latent Discriminant deterministic Uncertainty

Predictive uncertainty estimation is essential for deploying Deep Neural...

6 Gianni Franchi, et al. ∙

research

∙ 03/02/2022

MUAD: Multiple Uncertainties for Autonomous Driving benchmark for multiple uncertainty types and tasks

Predictive uncertainty estimation is essential for deploying Deep Neural...

7 Gianni Franchi, et al. ∙

research

∙ 02/17/2022

A study of deep perceptual metrics for image quality assessment

Several metrics exist to quantify the similarity between images, but the...

14 Rémi Kazmierczak, et al. ∙

research

∙ 09/17/2021

Efficient State Representation Learning for Dynamic Robotic Scenarios

While the rapid progress of deep learning fuels end-to-end reinforcement...

0 Zhaorun Chen, et al. ∙

research

∙ 07/05/2021

Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?

Affordances are the possibilities of actions the environment offers to t...

0 Hugo Caselles-Dupré, et al. ∙

research

∙ 07/05/2021

SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences

We introduce SCOD (Sensory Commutativity Object Detection), an active me...

7 Hugo Caselles-Dupré, et al. ∙

research

∙ 05/20/2021

Evaluating Robustness over High Level Driving Instruction for Autonomous Driving

In recent years, we have witnessed increasingly high performance in the ...

0 Florence Carton, et al. ∙

research

∙ 04/24/2021

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case

The latest Deep Learning (DL) models for detection and classification ha...

9 Natalia Díaz Rodríguez, et al. ∙

research

∙ 04/02/2021

Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey

Most of state of the art methods applied on time series consist of deep ...

0 Thomas Rojat, et al. ∙

research

∙ 05/13/2020

DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

Robots are still limited to controlled conditions, that the robot design...

22 Stéphane Doncieux, et al. ∙

research

∙ 02/13/2020

On the Sensory Commutativity of Action Sequences for Embodied Agents

We study perception in the scenario of an embodied agent equipped with f...

0 Hugo Caselles-Dupré, et al. ∙

research

∙ 12/06/2019

Regularization Shortcomings for Continual Learning

In classical machine learning, the data streamed to the algorithms is as...

0 Timothée Lesort, et al. ∙

research

∙ 07/11/2019

DisCoRL: Continual Reinforcement Learning via Policy Distillation

In multi-task reinforcement learning there are two main challenges: at t...

6 René Traoré, et al. ∙

research

∙ 06/29/2019

Continual Learning for Robotics

Continual learning (CL) is a particular machine learning paradigm where ...

0 Timothée Lesort, et al. ∙

research

∙ 06/11/2019

Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer

We focus on the problem of teaching a robot to solve tasks presented seq...

0 René Traoré, et al. ∙

research

∙ 03/30/2019

Symmetry-Based Disentangled Representation Learning requires Interaction with Environments

Finding a generally accepted formal definition of a disentangled represe...

0 Hugo Caselles-Dupré, et al. ∙

research

∙ 02/25/2019

S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay

We consider the problem of building a state representation model for con...

22 Hugo Caselles-Dupré, et al. ∙

research

∙ 01/24/2019

Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics

Scaling end-to-end reinforcement learning to control real robots from vi...

0 Antonin Raffin, et al. ∙

research

∙ 12/21/2018

Generative Models from the perspective of Continual Learning

Which generative model is the most suitable for Continual Learning? This...

16 Timothée Lesort, et al. ∙

research

∙ 10/31/2018

Don't forget, there is more than forgetting: new metrics for Continual Learning

Continual learning consists of algorithms that learn from a stream of da...

0 Natalia Díaz Rodríguez, et al. ∙

research

∙ 10/29/2018

Marginal Replay vs Conditional Replay for Continual Learning

We present a new replay-based method of continual classification learnin...

0 Timothée Lesort, et al. ∙

research

∙ 10/09/2018

Continual State Representation Learning for Reinforcement Learning using Generative Replay

We consider the problem of building a state representation model in a co...

0 Hugo Caselles-Dupré, et al. ∙

research

∙ 09/25/2018

S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning

State representation learning aims at learning compact representations f...

0 Antonin Raffin, et al. ∙

research

∙ 09/12/2018

Learning structure-from-motionfrom motion

This work is based on a questioning of the quality metrics used by deep ...

8 Clément Pinard, et al. ∙

research

∙ 09/12/2018

Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network

Using a neural network architecture for depth map inference from monocul...

0 Clément Pinard, et al. ∙

research

∙ 09/12/2018

End-to-end depth from motion with stabilized monocular videos

We propose a depth map inference system from monocular videos based on a...

4 Clément Pinard, et al. ∙

research

∙ 09/03/2018

Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning

We propose Flatland, a simple, lightweight environment for fast prototyp...

0 Hugo Caselles-Dupré, et al. ∙

research

∙ 06/28/2018

Training Discriminative Models to Evaluate Generative Ones

Generative models are known to be difficult to assess. Recent works, esp...

0 Timothée Lesort, et al. ∙

research

∙ 04/02/2018

Exploring to learn visual saliency: The RL-IAC approach

The problem of object localization and recognition on autonomous mobile ...

0 Céline Craye, et al. ∙

research

∙ 02/12/2018

State Representation Learning for Control: An Overview

Representation learning algorithms are designed to learn abstract featur...

0 Timothée Lesort, et al. ∙

research

∙ 09/15/2017

Unsupervised state representation learning with robotic priors: a robustness benchmark

Our understanding of the world depends highly on our capacity to produce...

0 Timothée Lesort, et al. ∙

David Filliat

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