检索结果-内蒙古大学图书馆

The geometry of bayesian programming

MATHEMATICAL STRUCTURES IN COMPUTER SCIENCE 2021年第6期31卷 633-681页

作者： Dal Lago, Ugo Hoshino, Naohiko Univ Bologna Dept Comp Sci & Engn Bologna Italy Sojo Univ Dept Comp & Informat Sci Kumamoto Japan

We give two geometry of interaction models for a typed lambda-calculus with recursion endowed with operators for sampling from a continuous uniform distribution and soft conditioning, namely a paradigmatic calculus for higher-order bayesian programming. The models are based on the category of measurable spaces and partial measurable functions, and the category of measurable spaces and s-finite kernels, respectively. The former is proved adequate with respect to both a distribution-based and a sampling-based operational semantics, while the latter is proved adequate with respect to a sampling-based operational semantics.

关键词： Probabilistic lambda-calculus conditioning bayesian programming geometry of interaction

来源：评论

学校读者我要写书评

暂无评论

Design of Stochastic Machines Dedicated to Approximate bayesian Inferences

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2019年第1期7卷 60-66页

作者： Faix, Marvin Laurent, Raphael Bessiere, Pierre Mazer, Emmanuel Droulez, Jacques CNRS F-75016 Paris France ProBaYes SAS F-38330 Saint Ismier France

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.

关键词： Stochastic computing bayesian programming

来源：评论

学校读者我要写书评

暂无评论

Cell signaling as a probabilistic computer

引用

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2017年 83卷 385-399页

作者： Colliaux, David Bessiere, Pierre Droulez, Jacques Sorbonne Univ CNRS UPMC ISIR Pyramide T55-65CC 173 4 Pl Jussieu F-75005 Paris France

Living organisms survive and multiply even though they have uncertain and incomplete information about their environment and imperfect models to predict the consequences of their actions. bayesian models have been proposed to face this challenge. Indeed, bayesian inference is a way to do optimal reasoning when only uncertain and incomplete information is available. Various perceptive, sensory-motor, and cognitive functions have been successfully modeled this way. However, the biological mechanisms allowing animals and humans to represent and to compute probability distributions are not known. It has been proposed that neurons and assemblies of neurons could be the appropriate scale to search for clues to probabilistic reasoning. In contrast, in this paper, we propose that interacting populations of macromolecules and diffusible messengers can perform probabilistic computation. This suggests that probabilistic reasoning, based on cellular signaling pathways, is a fundamental skill of living organisms available to the simplest unicellular organisms as well as the most complex brains. (C) 2016 Elsevier Inc. All rights reserved.

关键词： bayesian programming bayesian networks Cell signaling Unconventional bayesian Inference

来源：评论

学校读者我要写书评

暂无评论

Autonomous Robot Controller Using Bitwise GIBBS Sampling 15

Autonomous Robot Controller Using Bitwise GIBBS Sampling

引用

15th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Canillas, Remi Laurent, Raphael Faix, Marvin Vaufreydaz, Dominique Mazer, Emmanuel INSA Lyon LIG CNRS Villeurbanne France ProbaYes SAS Grenoble France Univ Grenoble Alpes CNRS LIG Grenoble France Univ Grenoble Alpes LIG Inria Grenoble France

ISBN: (纸本)9781509038466

In the present paper we describe a bio-inspired non von Neumann controller for a simple sensorimotor robotic system. This controller uses a bitwise version of the Gibbs sampling algorithm to select commands so the robot can adapt its course of action and avoid perceived obstacles in the environment. The VHDL specification of the circuit implementation of this controller is based on stochastic computation to perform bayesian inference at a low energy cost. We show that the proposed unconventional architecture allows to successfully carry out the obstacle avoidance task and to address scalability issues observed in previous works.

关键词： bayesian programming stochastic computing autonomous robots Gibbs sampling

来源：评论

学校读者我要写书评

暂无评论

COSMO ("Communicating about Objects using Sensory-Motor Operations"): A bayesian modeling framework for studying speech communication and the emergence of phonological systems

引用

JOURNAL OF PHONETICS 2015年 53卷 5-41页

作者： Moulin-Frier, Clement Diard, Julien Schwartz, Jean-Luc Bessiere, Pierre Grenoble Univ CNRS GIPSA Lab UMR5216 Grenoble France Grenoble Univ CNRS UMR 5105 Lab Psychol & NeuroCognit Grenoble France Coll France CNRS UMR 7152 Lab Physiol Percept & Act F-75231 Paris France Inria ENSTA Paristech Flowers Team Bordeaux France

While the origin of language remains a somewhat mysterious process, understanding how human language takes specific forms appears to be accessible by the experimental method. Languages, despite their wide variety, display obvious regularities. In this paper, we attempt to derive some properties of phonological systems (the sound systems for human languages) from speech communication principles. We introduce a model of the cognitive architecture of a communicating agent, called COSMO (for "Communicating about Objects using Sensory-Motor Operations') that allows a probabilistic expression of the main theoretical trends found in the speech production and perception literature. This enables a computational comparison of these theoretical trends, which helps us to identify the conditions that favor the emergence of linguistic codes. We present realistic simulations of phonological system emergence showing that COSMO is able to predict the main regularities in vowel, stop consonant and syllable systems in human languages. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Speech sound systems Language evolution Phonology Cognitive modelling Self-organization bayesian programming

来源：评论

学校读者我要写书评

暂无评论

Further Steps towards Driver Modeling According to the bayesian programming Approach

Further Steps towards Driver Modeling According to the Bayes...

引用

2nd International Conference on Digital Human Modeling held at the HCI International 2009

作者： Moebus, Claus Eilers, Mark Carl von Ossietzky Univ Oldenburg OFFIS D-2900 Oldenburg Germany

ISBN: (纸本)9783642028083

The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulating traffic scenarios. We describe first results to model lateral and longitudinal control behavior of drivers with simple e dynamic bayesian sensory-motor models according to the bayesian programming (BP) approach: bayesian Autonomous Driver (BAD) models. BAD models are learnt from multivariate time series of driving episodes generated by single or groups of users. The variables of the time series describe phenomena and processes of perception, cognition, and action control of drivers. BAD models reconstruct the joint probability distribution (JPD) of those variables by a composition of conditional probability distributions (CPDs). The real-time control of virtual vehicles is achieved by inferring the appropriate actions under the evidence of sensory percepts with the help of the reconstructed JPD.

关键词： digital human response models driver models bayesian autonomous driver models learning of human control strategies probabilistic bayesian lateral and longitudinal control graphical modeling human behavior learning and transfer bayesian programming

来源：评论

学校读者我要写书评

暂无评论

A bayesian Framework for Active Artificial Perception

引用

IEEE TRANSACTIONS ON CYBERNETICS 2013年第2期43卷 699-711页

作者： Ferreira, Joao Filipe Lobo, Jorge Bessiere, Pierre Castelo-Branco, Miguel Dias, Jorge Univ Coimbra Inst Syst & Robot P-3030290 Coimbra Portugal Univ Coimbra Fac Sci & Technol P-3030290 Coimbra Portugal Coll France CNRS F-75794 Paris 16 France Univ Coimbra Biomed Inst Res Light & Image Fac Med P-3030290 Coimbra Portugal Khalifa Univ Sci Technol & Res Abu Dhabi 127788 U Arab Emirates

In this paper, we present a bayesian framework for the active multimodal perception of 3-D structure and motion. The design of this framework finds its inspiration in the role of the dorsal perceptual pathway of the human brain. Its composing models build upon a common egocentric spatial configuration that is naturally fitting for the integration of readings from multiple sensors using a bayesian approach. In the process, we will contribute with efficient and robust probabilistic solutions for cyclopean geometry-based stereovision and auditory perception based only on binaural cues, modeled using a consistent formalization that allows their hierarchical use as building blocks for the multimodal sensor fusion framework. We will explicitly or implicitly address the most important challenges of sensor fusion using this framework, for vision, audition, and vestibular sensing. Moreover, interaction and navigation require maximal awareness of spatial surroundings, which, in turn, is obtained through active attentional and behavioral exploration of the environment. The computational models described in this paper will support the construction of a simultaneously flexible and powerful robotic implementation of multimodal active perception to be used in real-world applications, such as human-machine interaction or mobile robot navigation.

关键词： Active perception bayesian programming biologically inspired robots computer vision multimodal perception multisensory exploration sensing and perception sensor fusion

来源：评论

学校读者我要写书评

暂无评论

Using Gaussian Processes in bayesian Robot programming

引用

10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Aznar, Fidel Pujol, Francisco A. Pujol, Mar Rizo, Ramon Univ Alicante Dept Ciencia Computac & Inteligencia Artificial POB 99 E-03080 Alicante Spain Univ Alicante Dept Tecnologia Informat Comp Alicante Spain

ISBN: (纸本)9783642024801

In this paper, we present an adaptation of Gaussian Processes for learning a joint probabilistic distribution using bayesian programming. More specifically, a robot navigation problem will be showed as a case of study. In addition, Gaussian Processes will be compared with one of the most popular techniques for machine learning: Neural Networks. Finally, we will discuss about the accuracy of these methods and will conclude proposing some future lines for this research.

关键词： Machine Learning Gaussian Processes bayesian programming Robot Navigation

来源：评论

学校读者我要写书评

暂无评论

Brain-coupled interaction for semi-autonomous navigation of an assistive robot

引用

ROBOTICS AND AUTONOMOUS SYSTEMS 2010年第12期58卷 1246-1255页

作者： Perrin, Xavier Chavarriaga, Ricardo Colas, Francis Siegwart, Roland Millan, Jose del R. ETH Autonomous Syst Lab Zurich Switzerland Ecole Polytech Fed Lausanne CNBI Ctr Neuroprosthet CH-1015 Lausanne Switzerland

This paper presents a novel semi-autonomous navigation strategy designed for low throughput interfaces. A mobile robot (e.g. intelligent wheelchair) proposes the most probable action, as analyzed from the environment, to a human user who can either accept or reject the proposition. In the case of refusal, the robot will propose another action, until both entities agree on what needs to be done. In an unknown environment, the robotic system first extracts features so as to recognize places of interest where a human-robot interaction should take place (e.g. crossings). Based on the local topology, relevant actions are then proposed, the user providing answers by means of a button or a brain-computer interface (BCI). Our navigation strategy is successfully tested both in simulation and with a real robot, and a feasibility study for the use of a BCI confirms the potential of such an interface. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Semi-autonomous navigation Assistive robotics Low throughput user interface bayesian programming Brain-computer interface Neurorobotics EEG Error-related potentials

来源：评论

学校读者我要写书评

暂无评论

Simple Form Recognition Using bayesian programming

Simple Form Recognition Using Bayesian Programming

引用

9th International Conference on Intelligent Autonomous Systems (IAS-9)

作者： Ramel, Guy Tapus, Adriana Aspert, Francois Siegwart, Roland Ecole Polytech Fed Lausanne STI I2S U LSA ME A3 434 Batiment ME CH-1015 Lausanne Switzerland

ISBN: (纸本)9781586035952

The environment that surrounds us is very complex. Understanding and interpreting it is a very hard task. This paper proposes an approach allowing simple form recognition with a camera by using a probabilistic approach based on the bayesian programming formalism. The main goal is to recognize several type of elemental features composing an image, such as local orientation of a contour, or corners. The bayesian Program for feature recognition is presented and the learning stage explained. Our approach has been validated through experiments.

关键词： Computer Vision Pattern recognition bayesian programming Human-Robot Interaction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：