|
|
|
| e-Pub |
Previous | 
Home
Section: Partnerships and Cooperations
National Initiatives
ANR BNPSI
Participants : Pierre Chainais, Hong-Phuong Dang, Clément Elvira, Emmanuel Duflos, Philippe Vanheeghe.
-
Title: Bayesian Non Parametric approaches for Signal and Image Processing
-
Other Partners: Inria Bordeaux, team ALEA, Université de Bordeaux, IMS, Institut de Recherche en Indormatique de Toulouse (IRIT), CEA-LIST Saclay.
-
Abstract: Statistical methods have become more and more popular in signal and image processing over the past decades. These methods have been able to tackle various applications such as speech recognition, object tracking, image segmentation or restoration, classification, clustering, etc. We propose here to investigate the use of Bayesian nonparametric methods in statistical signal and image processing. Similarly to Bayesian parametric methods, this set of methods is concerned with the elicitation of prior and computation of posterior distributions, but now on infinite-dimensional parameter spaces. Although these methods have become very popular in statistics and machine learning over the last 15 years, their potential is largely underexploited in signal and image processing. The aim of the overall project, which gathers researchers in applied probabilities, statistics, machine learning and signal and image processing, is to develop a new framework for the statistical signal and image processing communities. Based on results from statistics and machine learning we aim at defining new models, methods and algorithms for statistical signal and image processing. Applications to hyperspectral image analysis, image segmentation, GPS localization, image restoration or space-time tomographic reconstruction will allow various concrete illustrations of the theoretical advances and validation on real data coming from realistic contexts.
-
Activity Report: This ANR Project was accepted in 2013. It has started in february 2014 on a new area of research for signal and image processing and is supervised by Pierre Chainais. Three meetings have taken place in Lille (in February), Toulouse (in June) and Bordeaux (in November). One special session on Bayesian non parametric approaches has been submitted and accepted to the international conference EUSIPCO 2015. We have also been selected by the Franch National Signal & Image Processing Society (GRETSI) to organize the Peyresq 2016 Signal processing summer school. Two PhD students have been recruited in october 2014 thanks to this project: Clément Elvira works in Lille is co-supervised by P. Chainais and N. Dobigeon (Toulouse), Jessica Sodjo works in Bordeaux and is co-supervised by A. Giremus (IMS), N. Dobigeon (Toulouse) and F. Caron (Oxford). Moreover, Hong-Phuong Dang (PhD, 2nd year) has obtained new results on BNP for dictionary learning. The Indian Buffet Process permits to propose a method to learn a dictionary of which size automatically adapts to data. Several publications are in preparation. François Caron who is co-leading this project with Pierre Chainais has moved to Oxford University as an Assistant Professor so that we will benefit from strong connections with the Statistics Departmnt in Oxford University.
ANR ExTra-Learn
Participants : Alessandro Lazaric, Jérémie Mary, Rémi Munos, Michal Valko.
-
Title: Extraction and Transfer of Knowledge in Reinforcement Learning
-
Abstract: ExTra-Learn is directly motivated by the evidence that one of the key features that allows humans to accomplish complicated tasks is their ability of building knowledge from past experience and transfer it while learning new tasks. We believe that integrating transfer of learning in machine learning algorithms will dramatically improve their learning performance and enable them to solve complex tasks. We identify in the reinforcement learning (RL) framework the most suitable candidate for this integration. RL formalizes the problem of learning an optimal control policy from the experience directly collected from an unknown environment. Nonetheless, practical limitations of current algorithms encouraged research to focus on how to integrate prior knowledge into the learning process. Although this improves the performance of RL algorithms, it dramatically reduces their autonomy. In this project we pursue a paradigm shift from designing RL algorithms incorporating prior knowledge, to methods able to incrementally discover, construct, and transfer “prior” knowledge in a fully automatic way. More in detail, three main elements of RL algorithms would significantly benefit from transfer of knowledge. (i) For every new task, RL algorithms need exploring the environment for a long time, and this corresponds to slow learning processes for large environments. Transfer learning would enable RL algorithms to dramatically reduce the exploration of each new task by exploiting its resemblance with tasks solved in the past. (ii) RL algorithms evaluate the quality of a policy by computing its state-value function. Whenever the number of states is too large, approximation is needed. Since approximation may cause instability, designing suitable approximation schemes is particularly critical. While this is currently done by a domain expert, we propose to perform this step automatically by constructing features that incrementally adapt to the tasks encountered over time. This would significantly reduce human supervision and increase the accuracy and stability of RL algorithms across different tasks. (iii) In order to deal with complex environments, hierarchical RL solutions have been proposed, where state representations and policies are organized over a hierarchy of subtasks. This requires a careful definition of the hierarchy, which, if not properly constructed, may lead to very poor learning performance. The ambitious goal of transfer learning is to automatically construct a hierarchy of skills, which can be effectively reused over a wide range of similar tasks.
-
Activity Report: ExTra-Learn started officially in October and one paper has been published at NIPS'14 and in the workshop on “Transfer and Multi-task Learning” at NIPS'14.
National Partners
-
Laboratoire Paul Painlevé Université des Sciences et Technologies de Lille, France
-
Laboratoire de Mécanique de Lille, Université de Lille 1, France.
-
Institut Carnot de Bourgogne, CNRS UMR 6303, Université de Bourgogne, Dijon, France.
-
LAGIS (CRIStAL), Ecole Centrale Lille - Université de Lille 1, France.