MINES ParisTech

research

∙ 11/25/2019

End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances

Reinforcement Learning (RL) aims at learning an optimal behavior policy ...

Marin Toromanoff, et al. ∙

research

∙ 10/27/2021

Learning from demonstrations with SACR2: Soft Actor-Critic with Reward Relabeling

During recent years, deep reinforcement learning (DRL) has made successf...

Jesus Bujalance Martin, et al. ∙

research

∙ 07/31/2019

Deep Sensor Fusion for Real-Time Odometry Estimation

Cameras and 2D laser scanners, in combination, are able to provide low-c...

Michelle Valente, et al. ∙

research

∙ 11/22/2021

Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping

Paris-CARLA-3D is a dataset of several dense colored point clouds of out...

Jean-Emmanuel Deschaud, et al. ∙

research

∙ 08/05/2019

3D Reconstruction of Deformable Revolving Object under Heavy Hand Interaction

We reconstruct 3D deformable object through time, in the context of a li...

Raoul de Charette, et al. ∙

research

∙ 06/01/2017

Supervised Quantile Normalisation

Quantile normalisation is a popular normalisation method for data subjec...

Marine Le Morvan, et al. ∙

research

∙ 06/26/2017

YoTube: Searching Action Proposal via Recurrent and Static Regression Networks

In this paper, we present YoTube-a novel network fusion framework for se...

Hongyuan Zhu, et al. ∙

research

∙ 06/14/2016

Digits that are not: Generating new types through deep neural nets

For an artificial creative agent, an essential driver of the search for ...

Akın Kazakçıand Mehdi Cherti, et al. ∙

research

∙ 03/09/2017

Prior-based Hierarchical Segmentation Highlighting Structures of Interest

Image segmentation is the process of partitioning an image into a set of...

Amin Fehri, et al. ∙

research

∙ 07/27/2016

Network-Guided Biomarker Discovery

Identifying measurable genetic indicators (or biomarkers) of a specific ...

Chloé-Agathe Azencott, et al. ∙

research

∙ 09/09/2016

Automatic Selection of Stochastic Watershed Hierarchies

The segmentation, seen as the association of a partition with an image, ...

Amin Fehri, et al. ∙

research

∙ 03/10/2015

Learning the Structure for Structured Sparsity

Structured sparsity has recently emerged in statistics, machine learning...

Nino Shervashidze, et al. ∙

research

∙ 02/09/2016

A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images

A general framework of spatio-spectral segmentation for multi-spectral i...

Guillaume Noyel, et al. ∙

research

∙ 05/12/2014

Consistency of random forests

Random forests are a learning algorithm proposed by Breiman [Mach. Learn...

Erwan Scornet, et al. ∙

research

∙ 12/23/2013

Using Latent Binary Variables for Online Reconstruction of Large Scale Systems

We propose a probabilistic graphical model realizing a minimal encoding ...

Victorin Martin, et al. ∙

research

∙ 01/26/2011

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial ...

Anne-Claire Haury, et al. ∙

research

∙ 10/05/2010

A bagging SVM to learn from positive and unlabeled examples

We consider the problem of learning a binary classifier from a training ...

Fantine Mordelet, et al. ∙

research

∙ 01/18/2010

Increasing stability and interpretability of gene expression signatures

Motivation : Molecular signatures for diagnosis or prognosis estimated f...

Anne-Claire Haury, et al. ∙

research

∙ 02/01/2012

The watershed concept and its use in segmentation : a brief history

The watershed is one of the most used tools in image segmentation. We pr...

Fernand Meyer, et al. ∙

research

∙ 03/13/2011

SO(3)-invariant asymptotic observers for dense depth field estimation based on visual data and known camera motion

In this paper, we use known camera motion associated to a video sequence...

Nadege Zarrouati, et al. ∙

research

∙ 01/23/2008

A path following algorithm for the graph matching problem

We propose a convex-concave programming approach for the labeled weighte...

Mikhail Zaslavskiy, et al. ∙

research

∙ 06/25/2017

An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles (Extended)

Before reaching full autonomy, vehicles will gradually be equipped with ...

Florent Altche, et al. ∙

research

∙ 02/04/2018

HPC Curriculum and Associated Ressources in the Academic Context

Hardware support for high-performance computing (HPC) has so far been su...

Claude Tadonki, et al. ∙

research

∙ 02/12/2018

Basic Parallel and Distributed Computing Curriculum

With the advent of multi-core processors and their fast expansion, it is...

Claude Tadonki, et al. ∙

research

∙ 02/28/2018

Machine learning and genomics: precision medicine vs. patient privacy

Machine learning can have major societal impact in computational biology...

Chloé-Agathe Azencott, et al. ∙

research

∙ 03/06/2018

Invariant Smoothing on Lie Groups

In this paper we propose a (non-linear) smoothing algorithm for group-af...

Paul Chauchat, et al. ∙

research

∙ 03/13/2018

Light Virtual Reality systems for the training of conditionally automated vehicle drivers

In conditionally automated vehicles, drivers can engage in secondary act...

Daniele Sportillo, et al. ∙

research

∙ 05/17/2018

Efficient simulation of Gaussian Markov random fields by Chebyshev polynomial approximation

This paper presents an algorithm to simulate Gaussian random vectors who...

Mike Pereira, et al. ∙

research

∙ 05/25/2018

Fusing Laser Scanner and Stereo Camera in Evidential Grid Maps

Automation driving techniques have seen tremendous progresses these last...

Michelle Valente, et al. ∙

research

∙ 06/22/2018

Polarized Rewriting and Tableaux in B Set Theory

We propose and extension of the tableau-based first-order automated theo...

Olivier Hermant, et al. ∙

research

∙ 10/22/2018

Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning

This paper explores the capability of deep neural networks to capture ke...

Guillaume Devineau, et al. ∙

research

∙ 11/22/2018

Building the Case for Temperature Awareness in Energy Consumption Models: an Application of the Energy-Frequency Convexity Rule

Optimizing computing and communication systems that host energy-critical...

Kameswar Vaddina, et al. ∙

research

∙ 11/30/2018

Multiview Based 3D Scene Understanding On Partial Point Sets

Deep learning within the context of point clouds has gained much researc...

Ye Zhu, et al. ∙

research

∙ 02/03/2019

MorteX method for contact along real and embedded surfaces: coupling X-FEM with the Mortar method

A method to treat frictional contact problems along embedded surfaces in...

Basava Raju Akula, et al. ∙

research

∙ 02/03/2019

Stabilized MorteX method for mesh tying along embedded interfaces

We present a unified framework to tie overlapping meshes in solid mechan...

Basava Raju Akula, et al. ∙

research

∙ 02/22/2019

An LSTM Network for Real-Time Odometry Estimation

The use of 2D laser scanners is attractive for the autonomous driving in...

Michelle Valente, et al. ∙

research

∙ 09/21/2019

ASNI: Adaptive Structured Noise Injection for shallow and deep neural networks

Dropout is a regularisation technique in neural network training where u...

Beyrem Khalfaoui, et al. ∙

research

∙ 10/03/2019

Path-planning microswimmers can swim efficiently in turbulent flows

We develop an adversarial-reinforcement learning scheme for microswimmer...

Jaya Kumar Alageshan, et al. ∙

research

∙ 01/07/2020

Generalized mean shift with triangular kernel profile

The mean shift algorithm is a popular way to find modes of some probabil...

Sébastien Razakarivony, et al. ∙

research

∙ 01/20/2020

OpenMP Parallelization of Dynamic Programming and Greedy Algorithms

Multicore has emerged as a typical architecture model since its advent a...

Claude Tadonki, et al. ∙

research

∙ 03/04/2020

Learning,Generating and Adapting Wave Gestures for Expressive Human-Robot Interaction

This study proposes a novel imitation learning approach for the stochast...

Mihalis Panteris, et al. ∙

research

∙ 05/26/2020

Hard Shape-Constrained Kernel Machines

Shape constraints (such as non-negativity, monotonicity, convexity) play...

Pierre-Cyril Aubin-Frankowski, et al. ∙

research

∙ 07/31/2020

Fast computation of all pairs of geodesic distances

Computing an array of all pairs of geodesic distances between the pixels...

Guillaume Noyel, et al. ∙

research

∙ 06/03/2021

Comparative study and limits of different level-set formulations for the modeling of anisotropic grain growth

Four different finite element level-set (FE-LS) formulations are compare...

Brayan Murgas, et al. ∙

research

∙ 09/04/2021

GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation

In this paper, we propose GOHOME, a method leveraging graph representati...

Thomas Gilles, et al. ∙

research

∙ 10/13/2021

THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling

In this paper, we propose THOMAS, a joint multi-agent trajectory predict...

Thomas Gilles, et al. ∙

research

∙ 10/15/2021

A new class of α-transformations for the spatial analysis of Compositional Data

Georeferenced compositional data are prominent in many scientific fields...

Lucia Clarotto, et al. ∙

research

∙ 11/29/2021

Buildings Classification using Very High Resolution Satellite Imagery

Buildings classification using satellite images is becoming more importa...

Mohammad Dimassi, et al. ∙

research

∙ 12/15/2021

Autoencoder-based background reconstruction and foreground segmentation with background noise estimation

Even after decades of research, dynamic scene background reconstruction ...

Bruno Sauvalle, et al. ∙

research

∙ 01/11/2022

Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks

During recent years, deep reinforcement learning (DRL) has made successf...

Jesus Bujalance Martin, et al. ∙

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