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Publications associées (161)

Low-rank and sparse subspace modeling of speech for DNN based acoustic modeling

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

Towards the goal of improving acoustic modeling for automatic speech recognition (ASR), this work investigates the modeling of senone subspaces in deep neural network (DNN) posteriors using low-rank and sparse modeling approaches. While DNN posteriors are ...

ELSEVIER SCIENCE BV2019

Learning Task Priorities from Demonstrations

Sylvain Calinon

As humanoid robots become increasingly popular, learning and control algorithms must take into account the new constraints and challenges inherent to these platforms, if we aim to fully exploit their potential. One of the most prominent of such aspects is ...

2019

Deriving Brain Myelin Water Fraction Maps from Relaxometry: a Data-Driven Approach

Jean-Philippe Thiran, Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Marco Pizzolato, Gian Franco Piredda, Reto Meuli, Jonas Richiardi

Currently, one of the gold-standard methods to obtain brain myelin water fraction (MWF) maps is the multi-echo spin-echo sequence. To overcome some of its limitations (e.g. long acquisition times), a data-driven approach for deriving MWF maps is proposed h ...

2019

Robot Learning with Task-Parameterized Generative Models

Sylvain Calinon

Task-parameterized models provide a representation of movement/behavior that can adapt to a set of task parameters describing the current situation encountered by the robot, such as location of objects or landmarks in its workspace. This paper gives an ove ...

Springer2018

Modeling Facial Geometry using Compositional VAEs

Pascal Fua, Timur Bagautdinov, Chenglei Wu

We propose a method for learning non-linear face geometry representations using deep generative models. Our model is a variational autoencoder with multiple levels of hidden variables where lower layers capture global geometry and higher ones encode more l ...

2018

Modeling Facial Geometry using Compositional VAEs

Pascal Fua, Timur Bagautdinov, Chenglei Wu

IEEE2018

Real-time DCT Learning-based Reconstruction of Neural Signals

Volkan Cevher, Cosimo Aprile, Rabeeh Karimi Mahabadi

Wearable and implantable body sensor network systems are one of the key technologies for continuous monitoring of patient’s vital health status such as temperature and blood pressure, and brain activity. Such devices are critical for early detection of eme ...

IEEE2018

Generative Models for Learning Robot Manipulation Skills from Humans

Ajay Kumar Tanwani

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to d ...

EPFL2018

Variational Methods for Human Modeling

Timur Bagautdinov

A large part of computer vision research is devoted to building models and algorithms aimed at understanding human appearance and behaviour from images and videos. Ultimately, we want to build automated systems that are at least as capable as people when i ...

EPFL2018

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Jean-Philippe Thiran

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples ...

Springer2018

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