Search

Your search keyword '"Jacques Droulez"' showing total 136 results
Start Over Author "Jacques Droulez"
136 results on '"Jacques Droulez"'

1. Decision making under uncertainty: a quasimetric approach.

Author

Steve N'Guyen, Clément Moulin-Frier, and Jacques Droulez

Subjects
Medicine, Science
Abstract

We propose a new approach for solving a class of discrete decision making problems under uncertainty with positive cost. This issue concerns multiple and diverse fields such as engineering, economics, artificial intelligence, cognitive science and many others. Basically, an agent has to choose a single or series of actions from a set of options, without knowing for sure their consequences. Schematically, two main approaches have been followed: either the agent learns which option is the correct one to choose in a given situation by trial and error, or the agent already has some knowledge on the possible consequences of his decisions; this knowledge being generally expressed as a conditional probability distribution. In the latter case, several optimal or suboptimal methods have been proposed to exploit this uncertain knowledge in various contexts. In this work, we propose following a different approach, based on the geometric intuition of distance. More precisely, we define a goal independent quasimetric structure on the state space, taking into account both cost function and transition probability. We then compare precision and computation time with classical approaches.

Published
2013
Full Text
View/download PDF

5. A Memristor-Based Bayesian Machine.

Author

Kamel-Eddine Harabi, Tifenn Hirtzlin, Clement Türck, Elisa Vianello, Raphaël Laurent, Jacques Droulez, Pierre Bessière, Jean-Michel Portal, Marc Bocquet, and Damien Querlioz

Published
2021

38. Bayesian time-domain multiple sound source localization for a stochastic machine

Author

Emmanuel Mazer, Laurent Girin, Marvin Faix, Raphael Frisch, Jacques Droulez, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CRISSP (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Signal processing, Computer science, Bayesian probability, 020206 networking & telecommunications, Statistical model, specific hardware, 02 engineering and technology, Acoustic source localization, Bayesian stochastic machine, time-domain processing, Bayesian inference, Robustness (computer science), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Multiple sound source localization, Time domain, Algorithm, ComputingMilieux_MISCELLANEOUS
Abstract

We propose a time-domain multiple sound source localization (SSL) method based on Bayesian inference. This method is specifically designed to run on the stochastic machines (SM) that we are currently developing to perform efficient low-level sensor signal processing with ultra-low power consumption. The proposed SSL method is divided into two main parts. First, a probabilistic model is run on 50 very short time frames (3. 75ms each) of multichannel recorded signals. Second, the results obtained on the different frames are fused to obtain a final localization map. Using the system in a supervised way allows to extract estimated source locations by selecting as many maxima as there are sources in the room. We explain how this method is implemented on a SM. Experiments are presented to illustrate the performance and robustness of the resulting system.

Published
2019
Full Text
View/download PDF

40. A Bayesian stochastic machine for sound source localization

Author

Laurent Girin, Raphaël Laurent, Pierre Bessière, Laurent Fesquet, Raphael Frisch, Emmanuel Mazer, Augustin Lux, Jacques Droulez, Marvin Faix, Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interpretation and Modelling of Images and Videos (PERCEPTION ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CRISSP (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interpretation and Modelling of Images and Videos (PERCEPTION), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), and ANR-11-LABX-0025-01,PERSYVAL-lab,Systèmes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Stochastic computing, Computer science, Stochastic process, Posterior probability, Bayesian probability, 02 engineering and technology, Acoustic source localization, Bayesian inference, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, 020201 artificial intelligence & image processing, 0305 other medical science, Likelihood function, Algorithm
Abstract

International audience; Compared to conventional processors, stochastic computing architectures have strong potential to speed up computation time and to reduce power consumption. We present such an architecture, called Bayesian Machine (BM), dedicated to solving Bayesian inference problems. Given a set of noisy signals provided by low-level sensors, a BM estimates the posterior probability distribution of an unknown target information. In the present study, a BM is used to solve a sound source localization (SSL) problem: the BM computes the probability distribution of the position of a sound source given acoustic signals captured by a set of microphones. Assuming free field wave propagation (no reverberations), we express the SSL problem as the maximization of a likelihood function fed with audio features provided by the time-frequency (TF) analysis of the captured audio waves. The proposed BM uses bitwise parallel sampling to fuse the resulting multi-channel information. As the number of channels to fuse is large, the standard BM architecture encounters the so-called " time dilution problem " (long delays are necessary to obtain valid samples). We tackle this problem by using max-normalization of the distributions combined with a periodic re-sampling of the bit streams after processing a reasonably small subset of evidences. Finally, we compare the localization performance of the proposed machine with the results obtained using a standard version of the machine. The re-sampling leads to an impressive acceleration factor of 10³ in the computation.

Published
2017

41. Approximation Enhancement for Stochastic Bayesian Inference

Author

Pierre Bessière, Joseph S. Friedman, Jacques Droulez, Jorge Lobo, Damien Querlioz, Institut d'électronique fondamentale (IEF), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and University of Coimbra

Subjects
Stochastic computing, Computer science, business.industry, Applied Mathematics, Inference, [SCCO.COMP]Cognitive science/Computer science, 020206 networking & telecommunications, 02 engineering and technology, 021001 nanoscience & nanotechnology, Bayesian inference, Machine learning, computer.software_genre, Theoretical Computer Science, Bayesian statistics, Approximate inference, Artificial Intelligence, Frequentist inference, 0202 electrical engineering, electronic engineering, information engineering, Statistical inference, Fiducial inference, Artificial intelligence, 0210 nano-technology, business, computer, Software
Abstract

International audience; Advancements in autonomous robotic systems have been impeded by the lack of a specialized computational hardware that makes real-time decisions based on sensory inputs. We have developed a novel circuit structure that efficiently approximates naïve Bayesian inference with simple Muller C-elements. Using a stochastic computing paradigm, this system enablesreal-time approximate decision-making with an area-energy-delay product nearly one billiontimes smaller than a conventional general-purpose computer. In this paper, we propose severaltechniques to improve the approximation of Bayesian inference by reducing stochastic bitstream autocorrelation. We also evaluate the effectiveness of these techniques for various naïve inference tasks and discuss hardware considerations, concluding that these circuits enable approximate Bayesian inferences while retaining orders-of-magnitude hardware advantagescompared to conventional general-purpose computers.

Published
2017
Full Text
View/download PDF

42. Bayesian Sensor Fusion with Fast and Low Power Stochastic Circuits

Author

Raphaël Laurent, Jorge Lobo, Jacques Droulez, Pierre Bessière, M. Awais Aslam, Emmanuel Mazer, Alexandre Coninx, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], and University of Coimbra

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Theoretical computer science, Computer science, 020208 electrical & electronic engineering, Bayesian probability, Probabilistic logic, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Energy consumption, Sensor fusion, Bayesian inference, 020202 computer hardware & architecture, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Hardware Architecture, Computer engineering, Probabilistic logic network, Logic gate, 0202 electrical engineering, electronic engineering, information engineering, Applications of artificial intelligence
Abstract

International audience; —As the physical limits of Moore's law are being reached, a research effort is launched to achieve further performance improvements by exploring computation paradigms departing from standard approaches. The BAMBI project (Bottom-up Approaches to Machines dedicated to Bayesian Inference) aims at developing hardware dedicated to probabilistic computation , which extends logic computation realised by boolean gates in current computer chips. Such probabilistic computing devices would allow to solve faster and at a lower energy cost a wide range of Artificial Intelligence applications, especially when decisions need to be taken from incomplete data in an uncertain environment. This paper describes an architecture where very simple operators compute on a time coding of probability values as stochastic signals. Simulation tests and a reconfigurable logic hardware implementation demonstrated the feasibility and performances of the proposed inference machine. Hardware results show this architecture can quickly solve Bayesian sensor fusion problems and is very efficient in terms of energy consumption.

Published
2016

43. Dispositif de génération d’un signal électrique aléatoire et architecture associée

Author

Daniele Pinna, Julie Grollier, Vincent Cros, Damien Querlioz, Pierre Bessière, Jacques Droulez, Institut d'électronique fondamentale (IEF), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Unité mixte de physique CNRS/Thales (UMPhy CNRS/THALES), THALES [France]-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-THALES, and Bessiere, Pierre

Subjects
[SCCO.COMP] Cognitive science/Computer science, [SCCO.COMP]Cognitive science/Computer science
Published
2016

45. Quick and energy-efficient bayesian computing of binocular disparity using stochastic digital signals

Author

Jacques Droulez, Alexandre Coninx, Pierre Bessière, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), European Project: 618024,EC:FP7:ICT,FP7-ICT-2013-C,BAMBI(2014), Coninx, Alexandre, and Bottom-up Approaches to Machines dedicated to Bayesian Inference - BAMBI - - EC:FP7:ICT2014-01-01 - 2016-12-31 - 618024 - VALID

Subjects
FOS: Computer and information sciences, 0209 industrial biotechnology, Theoretical computer science, Computer Science - Artificial Intelligence, Computer science, Computer Vision and Pattern Recognition (cs.CV), Bayesian probability, Bayesian inference, Computer Science - Computer Vision and Pattern Recognition, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Theoretical Computer Science, 020901 industrial engineering & automation, [SCCO.COMP] Cognitive science/Computer science, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], energy efficiency, Stochastic computing, business.industry, Applied Mathematics, Probabilistic logic, Robotics, sensorimotor processing, Artificial Intelligence (cs.AI), Probability distribution, Binocular disparity, binocular disparity, stochastic computing, 020201 artificial intelligence & image processing, Distributed memory, Artificial intelligence, hardware implementation, business, Software
Abstract

Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a Bayesian inference problem. However, computation of the full disparity distribution with an advanced Bayesian model is usually an intractable problem, and proves computationally challenging even with a simple model. In this paper, we show how probabilistic hardware using distributed memory and alternate representation of data as stochastic bitstreams can solve that problem with high performance and energy efficiency. We put forward a way to express discrete probability distributions using stochastic data representations and perform Bayesian fusion using those representations, and show how that approach can be applied to diparity computation. We evaluate the system using a simulated stochastic implementation and discuss possible hardware implementations of such architectures and their potential for sensorimotor processing and robotics., Preprint of article submitted for publication in International Journal of Approximate Reasoning and accepted pending minor revisions

Published
2016
Full Text
View/download PDF

46. Keeping Track of Invisible Individuals While Exploring a Spatial Layout with Partial Cues: Location-based and Deictic Direction-based Strategies

Author

Jacques Droulez and Nicolas J. Bullot

Subjects
Computer science, business.industry, media_common.quotation_subject, Cognition, Perceptual inference, Deixis, Travelling salesman problem, Predicate (grammar), Philosophy, Human–computer interaction, Perception, Artificial intelligence, business, Applied Psychology, media_common
Abstract

In contrast to Constructivist Views, which construe perceptual cognition as an essentially reconstructive process, this article recommends the Deictic View, which grounds perception in perceptual-demonstrative reference and the use of deictic tracking strategies for acquiring and updating knowledge about individuals. The view raises the problem of how sensory-motor tracking connects to epistemic and integrated forms of tracking. To study the strategies used to solve this problem, we report a study of the ability to track distal individuals when only their directions can be perceived and not their locations. We introduce a new experimental paradigm named the ‘Modified Traveling Salesman Problem’ (MTSP), which requires subjects to visit n invisible targets in a 2D display once each. Surprisingly, subjects are competent at this task for up to 10 targets. We consider two types of tracking strategies that subjects might use: ‘location-based’ strategies and ‘deictic direction-based’ strategies. A number of obse...

Published
2008
Full Text
View/download PDF

47. Design of Stochastic Machines Dedicated to Approximate Bayesian inferences

Author

Jacques Droulez, Pierre Bessière, Raphaël Laurent, Marvin Faix, Emmanuel Mazer, PERVASIVE INTERACTION (PERVASIVE INTERACTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), European Project: 618024,EC:FP7:ICT,FP7-ICT-2013-C,BAMBI(2014), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Theoretical computer science, Computer science, Bayesian probability, Inference, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, 01 natural sciences, Bayesian Programming, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], symbols.namesake, Joint probability distribution, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Stochastic computing, 010308 nuclear & particles physics, 020206 networking & telecommunications, Computer Science Applications, Human-Computer Interaction, Bayesian statistics, symbols, stochastic computing, Stochastic optimization, Information Systems, Gibbs sampling, Von Neumann architecture
Abstract

International audience; We present an architecture and a compilation toolchain for stochastic machines dedicated to Bayesian inferences. These machines are not Von Neumann and code information with stochastic bitstreams instead of using floating point representations. They only rely on stochastic arithmetic and on Gibbs sampling to perform approximate inferences. They use banks of binary random generators which capture the prior knowledge on which the inference is built. The output of the machine is devised to continuously sample the joint probability distribution of interest. While the method is explained on a simple example, we show that our machine computes a good approximation of the solution to a problem intractable in exact inference.

Published
2016
Full Text
View/download PDF

48. Bayesian Inference With Muller C-Elements

Author

Pierre Bessière, Damien Querlioz, Jacques Droulez, Joseph S. Friedman, L. E. Calvet, Institut d'électronique fondamentale (IEF), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, and Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010302 applied physics, Computer science, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, 02 engineering and technology, 021001 nanoscience & nanotechnology, Bayesian inference, Machine learning, computer.software_genre, 01 natural sciences, Bayes' theorem, Naive Bayes classifier, Frequentist inference, Component (UML), 0103 physical sciences, Feature (machine learning), Influence diagram, Leverage (statistics), [INFO]Computer Science [cs], Artificial intelligence, Electrical and Electronic Engineering, 0210 nano-technology, business, computer
Abstract

International audience; Bayesian inference is a powerful approach for integrating independent conflicting information for decision-making. Though an important component of robotic, biological, and other sensory-motors systems, general-purpose computers perform Bayesian inference with limited efficiency. Here we show that Bayesian inference can be efficiently performed with stochastic signals, which are particularly adapted to novel low power nano-devices that exhibit faults and device variations. A simple Muller C-element directly implements Bayes' rule. Complex inferences are performed by C-element trees, which compute the probability of an event based on multiple independent sources of evidence. A naïve Bayesian spam filter circuit is demonstrated as a pedagogical application, and design techniques for improving circuit functionality are described. Limitations of this structure are discussed in terms of signal autocorrelation. The stochastic inference structure is exceptionally robust to faults, an essential feature of decision circuits, and can therefore leverage the increased efficiency of emerging nanodevices. This hardware implementation of Bayesian inference is extremely area and power efficient, with an area-energy-delay product several orders of magnitude less than the conventional floating point implementation. These results open a pathway for a direct stochastic hardware implementation of Bayesian inference, enabling a new class of embedded decision circuits for robotics and medical applications.

Published
2016
Full Text
View/download PDF

49. Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

Author

Gilles Reymond, Jacques Droulez, D. Toffin, and Andras Kemeny

Subjects
Engineering, business.industry, Mechanical Engineering, Internal model, Driving simulator, Neuromuscular transmission, Steering wheel, Vehicle dynamics, Acceleration, Automotive Engineering, Safety, Risk, Reliability and Quality, business, Torque steering, Simulation, Haptic technology
Abstract

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on...

Published
2007
Full Text
View/download PDF

50. Robust Fusion of Trackers using on-line Drift Prediction

Author

Jacques Droulez, Stéphane Herbin, Isabelle Leang, Benoît Girard, ONERA - The French Aerospace Lab [Palaiseau], ONERA-Université Paris Saclay (COmUE), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, and Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Fusion, BitTorrent tracker, business.industry, Computer science, [SCCO.NEUR]Cognitive science/Neuroscience, Process (computing), 020207 software engineering, Context (language use), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Video tracking, 0202 electrical engineering, electronic engineering, information engineering, Fuse (electrical), [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Noise (video), Artificial intelligence, business, Set (psychology), ComputingMilieux_MISCELLANEOUS
Abstract

Visual object tracking is a standard function of computer vision that has been the source of numerous propositions. This diversity of approaches leads to the idea of trying to fuse them and take advantage of the strengths of each of them while controlling the noise they may introduce in some circumstances. The work presented here describes a generic framework for the combination of trackers, where fusion may occur at different levels of the processing chain. The fusion process is governed by the online detection of abnormal behavior either from specific features provided by each tracker, or from out of consensus detection. The fusion of three trackers exploiting complementary designs and features is evaluated on 72 fusion schemes. Thorough experiments on 12 standard video sequences and on a new set of 13 videos addressing typical difficulties faced by vision systems used in the demanding context of driving assistance, show that using fusion improves greatly the performance of each individual tracker, and reduces by a factor two the probability of drifting.

Published
2015
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results