检索结果-内蒙古大学图书馆

DERIVING PROBABILITY DENSITY FUNCTIONS FROM probabilistic FUNCTIONAL PROGRAMS

LOGICAL METHODS IN COMPUTER SCIENCE 2017年第2期13卷

作者： Bhat, Sooraj Borgstrom, Johannes Gordon, Andrew D. Russo, Claudio Georgia Inst Technol Atlanta GA 30332 USA Uppsala Univ Uppsala Sweden Microsoft Res Redmond WA USA Univ Edinburgh Edinburgh Midlothian Scotland

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with failure and both discrete and continuous distributions, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework.

关键词： probability density function probabilistic programming Markov chain Monte Carlo

来源：评论

学校读者我要写书评

暂无评论

BayesPy: Variational Bayesian Inference in Python

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2016年第1期17卷 1419-1424页

作者： Luttinen, Jaakko Aalto Univ Dept Comp Sci Espoo Finland

BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model construction and efficient inference make BayesPy suitable for both average and expert Bayesian users. It also supports some advanced methods such as stochastic and collapsed variational inference.

关键词： variational Bayes probabilistic programming Python

来源：评论

学校读者我要写书评

暂无评论

probabilistic machine learning for breast cancer classification

引用

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第1期20卷 624-655页

作者： Leventi-Peetz, Anastasia -Maria Weber, Kai Fed Off Informat Secur Postfach 200363 D-53133 Bonn Germany Inducto Daten & Informat Syst GmbH D-84405 Dorfen Germany

A probabilistic neural network has been implemented to predict the malignancy of breast cancer cells, based on a data set, the features of which are used for the formulation and training of a model for a binary classification problem. The focus is placed on considerations when building the model, in order to achieve not only accuracy but also a safe quantification of the expected uncertainty of the calculated network parameters and the medical prognosis. The source code is included to make the results reproducible, also in accordance with the latest trending in machine learning research, named Papers with Code. The various steps taken for the code development are introduced in detail but also the results are visually displayed and critically analyzed also in the sense of explainable artificial intelligence. In statistical-classification problems, the decision boundary is the region of the problem space in which the classification label of the classifier is ambiguous. Problem aspects and model parameters which influence the decision boundary are a special aspect of practical investigation considered in this work. Classification results issued by technically transparent machine learning software can inspire more confidence, as regards their trustworthiness which is very important, especially in the case of medical prognosis. Furthermore, transparency allows the user to adapt models and learning processes to the specific needs of a problem and has a boosting influence on the development of new methods in relevant machine learning fields (transfer learning).

关键词： Bayesian neural networks decision boundary explainable artificial intelligence machine learning model uncertainty posterior predictive check prior probability probabilistic programming scatter plot variational posterior distribution

来源：评论

学校读者我要写书评

暂无评论

Relational affordances for multiple-object manipulation

引用

AUTONOMOUS ROBOTS 2018年第1期42卷 19-44页

作者： Moldovan, Bogdan Moreno, Plinio Nitti, Davide Santos-Victor, Jose De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci Leuven Belgium Univ Lisbon Inst Super Tecn LARSyS Inst Syst & Robot ISR IST Lisbon Portugal

The concept of affordances has been used in robotics to model action opportunities of a robot and as a basis for making decisions involving objects. Affordances capture the interdependencies between the objects and their properties, the executed actions on those objects, and the effects of those respective actions. However, existing affordance models cannot cope with multiple objects that may interact during action execution. Our approach is unique in that possesses the following four characteristics. First, our model employs recent advances in probabilistic programming to learn affordance models that take into account (spatial) relations between different objects, such as relative distances. Two-object interaction models are first learned from the robot interacting with the world in a behavioural exploration stage, and are then employed in worlds with an arbitrary number of objects. The model thus generalizes over both the number of and the particular objects used in the exploration stage, and it also effectively deals with uncertainty. Secondly, rather than using a (discrete) action repertoire, the actions are parametrised according to the motor capabilities of the robot, which allows to model and achieve goals at several levels of complexity. It also supports a two-arm parametrised action. Thirdly, the relational affordance model represents the state of the world using both discrete (action and object features) and continuous (effects) random variables. The effects follow a multivariate Gaussian distribution with the correlated discrete variables (actions and object properties). Fourthly, the learned model can be employed on planning for high-level goals that closely correspond to goals formulated in natural language. The goals are specified by means of (spatial) relations between the objects. The model is evaluated in real experiments using an iCub robot given a series of such planning goals of increasing difficulty.

关键词： Affordances Relational affordances probabilistic programming Object manipulation Planning

来源：评论

学校读者我要写书评

暂无评论

probabilistic Compute-in-Memory Design for Efficient Markov Chain Monte Carlo Sampling

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第2期71卷 703-716页

作者： Fu, Yihan Shi, Daijing Fan, Anjunyi Yue, Wenshuo Yang, Yuchao Huang, Ru Yan, Bonan Peking Univ Inst Artificial Intelligence Sch IntegratedCircuits Beijing 100871 Peoples R China Peking Univ Beijing Adv Innovat Ctr Integrated Circuits Sch Integrated Circuits Beijing 100871 Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen 518055 Peoples R China Chinese Inst Brain Res CIBR Beijing 102206 Peoples R China

Markov chain Monte Carlo (MCMC) is a widely used sampling method in modern artificial intelligence and probabilistic computing systems. It involves repetitive random number generations and thus often dominates the latency of probabilistic model computing. Hence, we propose a compute-in-memory (CIM) based MCMC design as a hardware acceleration solution. This work investigates SRAM bitcell stochasticity and proposes a novel "pseudo-read" operation, based on which we offer a block-wise random number generation circuit scheme for fast random number generation. Moreover, this work proposes a novel multi-stage exclusive-OR gate (MSXOR) design method to generate strictly uniformly distributed random numbers. The probability error deviating from a uniform distribution is suppressed under 10(-6) . Also, this work presents a novel in-memory copy circuit scheme to realize data copy inside a CIM sub-array, significantly reducing the use of R/W circuits for power saving. Evaluated in a commercial 28-nm process development kit, this CIM-based MCMC design generates 4-bit similar to 32-bit samples with an energy efficiency of 0.53 pJ/sample and high throughput of up to 1066.7M samples/s. Compared to conventional processors, the overall energy efficiency improves 2.12 x 10(9) to 9.58 x 10(9) times.

关键词： SRAM processing-in-memory Markov chain Monte Carlo sampling MCMC accelerator probabilistic programming random number generator arbitrary distribution

来源：评论

学校读者我要写书评

暂无评论

An exact algorithm for the maximum probabilistic clique problem

引用

JOURNAL OF COMBINATORIAL OPTIMIZATION 2014年第1期28卷 105-120页

作者： Miao, Zhuqi Balasundaram, Balabhaskar Pasiliao, Eduardo L. Oklahoma State Univ Sch Ind Engn & Management Stillwater OK 74078 USA Air Force Res Lab Munit Directorate Eglin AFB FL 32542 USA

The maximum clique problem is a classical problem in combinatorial optimization that has a broad range of applications in graph-based data mining, social and biological network analysis and a variety of other fields. This article investigates the problem when the edges fail independently with known probabilities. This leads to the maximum probabilistic clique problem, which is to find a subset of vertices of maximum cardinality that forms a clique with probability at least , which is a user-specified probability threshold. We show that the probabilistic clique property is hereditary and extend a well-known exact combinatorial algorithm for the maximum clique problem to a sampling-free exact algorithm for the maximum probabilistic clique problem. The performance of the algorithm is benchmarked on a test-bed of DIMACS clique instances and on a randomly generated test-bed.

关键词： Maximum clique problem probabilistic programming probabilistic clique Branch-and-bound

来源：评论

学校读者我要写书评

暂无评论

Trace Types and Denotational Semantics for Sound Programmable Inference in probabilistic Languages

引用

PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2020年第POPL期4卷 1–32页

作者： Lew, Alexander K. Cusumano-Towner, Marco F. Sherman, Benjamin Carbin, Michael Mansinghka, Vikash K. MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA

Modern probabilistic programming languages aim to formalize and automate key aspects of probabilistic modeling arid inference. Many languages provide constructs for programmable inference that enable developers to improve inference speed and accuracy by tailoring an algorithm for use with a particular model or dataset. Unfortunately, it is easy to use these constructs to write unsound programs that appear to run correctly but produce incorrect results. To address this problem, we present a denotational semantics for programmable inference in higher-order probabilistic programming languages, along with a type system that ensures that well-typed inference programs are sound by construction. A central insight is that the type of a probabilistic expression can track the space of its possible execution traces, not just the type of value that it returns. as these traces are often the objects that inference algorithms manipulate. We use our semantics and type system to establish soundness properties of custom inference programs that use constructs for variational, sequential Monte Carlo, importance sampling, and Markov chain Monte Carlo inference.

关键词： probabilistic programming type systems programmable inference

来源：评论

学校读者我要写书评

暂无评论

Automated quantized inference for probabilistic programs with AQUA

引用

INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2022年第3期18卷 369-384页

作者： Huang, Zixin Dutta, Saikat Misailovic, Sasa Univ Illinois Dept Comp Sci 201 North Goodwin Ave Urbana IL 61801 USA

We present AQUA, a new probabilistic inference algorithm that operates on probabilistic programs with continuous posterior distributions. AQUA approximates programs via an efficient quantization of the continuous distributions. It represents the distributions of random variables using quantized value intervals (Interval Cube) and corresponding probability densities (Density Cube). AQUA's analysis transforms Interval and Density Cubes to compute the posterior distribution with bounded error. We also present an adaptive algorithm for selecting the size and the granularity of the Interval and Density Cubes. We evaluate AQUA on 24 programs from the literature. AQUA solved all of 24 benchmarks in less than 43s (median 1.35s) with a high level of accuracy. We show that AQUA is more accurate than state-of-the-art approximate algorithms (Stan's NUTS and ADVI) and supports programs that are out of reach of exact inference tools, such as PSI and SPPL.

关键词： probabilistic programming probabilistic inference Quantization

来源：评论

学校读者我要写书评

暂无评论

A Pre-expectation Calculus for probabilistic Sensitivity

引用

PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2021年第POPL期5卷 1–28页

作者： Aguirre, Alejandro Barthe, Gilles Hsu, Justin Kaminski, Benjamin Lucien Katoen, Joost-Pieter Matheja, Christoph IMDEA Software Inst Madrid Spain Univ Politecn Madrid Madrid Spain MPI SP Saarbrucken Germany Univ Wisconsin Madison Madison WI USA UCL London England Rhein Westfal TH Aachen Aachen Germany Swiss Fed Inst Technol Zurich Switzerland

Sensitivity properties describe how changes to the input of a program affect the output, typically by upper bounding the distance between the outputs of two runs by a monotone function of the distance between the corresponding inputs. When programs are probabilistic, the distance between outputs is a distance between distributions. The Kantorovich lifting provides a general way of defining a distance between distributions by lifting the distance of the underlying sample space;by choosing an appropriate distance on the base space, one can recover other usual probabilistic distances, such as the Total Variation distance. We develop a relational pre-expectation calculus to upper bound the Kantorovich distance between two executions of a probabilistic program. We illustrate our methods by proving algorithmic stability of a machine learning algorithm, convergence of a reinforcement learning algorithm, and fast mixing for card shuffling algorithms. We also consider some extensions: using our calculus to show convergence of Markov chains to the uniform distribution over states and an asynchronous extension to reason about pairs of program executions with different control flow.

关键词： probabilistic programming verification

来源：评论

学校读者我要写书评

暂无评论

INTEGRATING MULTIPLE LONG-RANGE TRANSPORT MODELS INTO OPTIMIZATION METHODOLOGIES FOR ACID-RAIN POLICY ANALYSIS

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1990年第3期46卷 313-321页

作者： ELLIS, JH JOHNS HOPKINS UNIV DEPT GEOG & ENVIRONM ENGNBALTIMOREMD 21218 USA

This paper addresses certain complications inherent in developing efficient acid rain control strategies given that our knowledge of the physics and chemistry of long-range pollutant transport and transformation is imprecise. This imprecision (or uncertainty) translates, in a sense, into risk. We seek here to demonstrate that there are currently available methods to better define this risk and provide a basis for decision-making. More specifically, these methods involve chance-constrained stochastic programming techniques. A distinguishing characteristic of the approach is our use of the output from multiple long-range transport models to develop probabilistic descriptors of transport/transformation phenomena. [ABSTRACT FROM AUTHOR]

关键词： Acid rain environment probabilistic programming resource

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：