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检索条件"主题词=Probabilistic Graphical Models"

共 399 条记录，以下是321-330 订阅

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A probabilistic Evolutionary Optimization Approach to Compute Quasiparticle Braids

A Probabilistic Evolutionary Optimization Approach to Comput...

引用

10th International Conference on Simulated Evolution and Learning (SEAL)

作者： Santana, Roberto McDonald, Ross B. Katzgraber, Helmut G. Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Guipuzcoa 20018 Spain Texas A&M Univ Dept Phys & Astron College Stn TX 77843 USA Santa Fe Inst Santa Fe NM 87501 USA

ISBN: (纸本)9783319135632;9783319135625

This paper proposes the use of estimation of distribution algorithms to deal with the problem of finding an optimal product of braid generators in topological quantum computing. We investigate how the regularities of the braid optimization problem can be translated into statistical regularities by means of the Boltzmann distribution. The introduced algorithm obtains solutions with an accuracy in the order of 10(-6), and lengths up to 9 times shorter than those expected from braids of the same accuracy obtained with other methods.

关键词： topological computing quasiparticle braids probabilistic graphical models EDAs braid optimization Fibonacci anyons

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Computation Mechanism for Situated Sentient Robot

Computation Mechanism for Situated Sentient Robot

引用

Computing Conference

作者： Stipancic, Tomislav Ohmoto, Yoshimasa Badssi, Sara Akaoka Nishida, Toyoaki Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9781509054435

Emotions shape how we remember our world, how we perceive it, and which decisions we take. This study proposed a novel three-component computation mechanism that enables a robot to reason about emotions. The mechanism contains the following parts: (i) information acquisition, (ii) formal knowledge description in a form of ontology, and (iii) Bayesian Network (BN). An entropy reduction method from information theory is used to design an effective Human-Robot Interaction (HRI) and to gain a deeper understanding about proposed reasoning mechanism. The method also revealed the most influential BN variables that efficiently resolve the reasoning ambiguities. The modified OCC model of emotions in BN is implemented to ensure adaptation of the system to multiple sources of uncertainty. Variables in a hand crafted BN are linked together, where each link is quantified by spreading influences of a different strength that parent nodes have on child nodes. The paper introduces also the system architecture for realization of the physical robot setup.

关键词： affective robotics emotions context-awareness probabilistic graphical models ontology ubiquitous computing

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Partial Evaluation in Junction Trees 25

Partial Evaluation in Junction Trees

引用

25th Euromicro Conference on Digital System Design (DSD)

作者： Villescas, Martin Roa Wijnings, Patrick W. A. Stuijk, Sander Corporaal, Henk Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Sorama Eindhoven Netherlands

ISBN: (纸本)9781665474047

One prominent method to perform inference on probabilistic graphical models is the probability propagation in trees of clusters (PPTC) algorithm. In this paper, we demonstrate the use of partial evaluation, an established technique from the compiler domain, to improve the performance of online Bayesian inference using the PPTC algorithm in the context of observed evidence. We present a metaprogramming-based method to transform a base program into an optimized version by precomputing the static input at compile time while guaranteeing behavioral equivalence. We achieve an inference time reduction of 21% on average for the Promedas benchmark.

关键词： junction trees partial evaluation Bayesian inference probabilistic graphical models message passing

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Encoding local correspondence in Topic models

Encoding local correspondence in Topic Models

引用

25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Rochd, El Mehdi Quafafou, Mohamed Aznag, Mustapha Aix Marseille Univ LSIS UMR 7296 Marseille France

ISBN: (纸本)9781479929719

Exploiting label correlations is a challenging and crucial problem especially in multi-label learning context. Labels correlations are not necessarily shared by all instances and have generally a local definition. This paper introduces LOC-LDA, which is a latent variable model that adresses the problem of modeling annotated data by locally exploiting correlations between annotations. In particular, we represent explicitly local dependencies to define the correspondence between specific objects, i.e. regions of images and their annotations. We conducted experiments on a collection of pictures provided by the Wikipedia "Picture of the day" website (1), and evaluated our model on the task of "automatic image annotation". The results validate the effectiveness of our approach.

关键词： Topic models Automatic Image Annotation Local Influence probabilistic graphical models

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A computational model for solving raven's progressive matrices intelligence test

A computational model for solving raven's progressive matric...

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作者： Shegheva, Snejana Georgia Institute of Technology

学位级别：硕士

graphical models offer techniques for capturing the structure of many problems in real- world domains and provide means for representation, interpretation, and inference. The modeling framework provides tools for discovering rules for solving problems by exploring structural relationships. We present the Structural Affinity method that uses graphical models for first learning and subsequently recognizing the pattern for solving problems on the Raven's Progressive Matrices Test of general human intelligence. Recently there has been considerable work on computational models of addressing the Raven's test using various representations ranging from fractals to symbolic structures. In contrast, our method uses Markov Random Fields parameterized by affinity factors to discover the structure in the geometric problems and induce the rules of Carpenter et al.'s cognitive model of problem-solving on the Raven's Progressive Matrices Test. We provide a computational account that first learns the structure of Raven's problem and then predicts the solution by computing the probability of the correct answer by recognizing patterns corresponding to Carpenter et al.'s rules. We demonstrate that the performance of our model on the Standard Raven Progressive Matrices is comparable with existing state of the art models. In this report, we raise and attempt to address research questions about the knowledge representation that provides a sufficient opportunity to capture a pattern in geometrical intelligence tests such as Raven Progressive Matrices. We show how a minimal representation facilitates pattern extraction process by proposing a method for organizing the representational units using the framework of probabilistic graphical models. By orchestrating techniques from mathematics, data science and computer science, we design an agent that can explain its responses while still predicting accurate solutions. And finally, we discuss the key takeaways about knowledge representation, str

关键词： probabilistic graphical models Machine learning

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Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov models 48

Learning Dependencies of Discrete Speech Representations wit...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yeh, Sung-Lin Tang, Hao University of Edinburgh School of Informatics United Kingdom

ISBN: (纸本)9781728163277

While discrete latent variable models have had great success in self-supervised learning, most models assume that frames are independent. Due to the segmental nature of phonemes in speech perception, modeling dependencies among latent variables at the frame level can potentially improve the learned representations on phonetic-related tasks. In this work, we assume Markovian dependencies among latent variables, and propose to learn speech representations with neural hidden Markov models. Our general framework allows us to compare to self-supervised models that assume independence, while keeping the number of parameters fixed. The added dependencies improve the accessibility of phonetic information, phonetic segmentation, and the cluster purity of phones, showcasing the benefit of the assumed dependencies. © 2023 IEEE.

关键词： hidden Markov models probabilistic graphical models segmental structure self-supervised learning

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Wind Farm Layout Optimization Using Approximate Inference in graphical models

Wind Farm Layout Optimization Using Approximate Inference in...

引用

作者： Dhoot, Aditya University of Toronto

学位级别：master

Wind farm layout optimization (WFLO) determines the optimal location of wind turbines within a fixed geographical area to maximize the total power capacity of the wind farm, under stochastic wind conditions and non-linear aerodynamic interferences between turbines. This thesis develops optimization approaches to fast approximate (sub-optimal) turbine layouts to aide engineers make design decisions. Building on previous work in discrete quadratic WFLO models, we recast the program as a probabilistic graphical model incorporating spatial dependencies (i.e., aerodynamic interferences, proximity constraints, and maximum number of turbines) between the variables. Turbine layouts are estimated using message passing inference (BP, TRW-S), which exploit the problema s graph-theoretic structure using decomposition and factorization. We perform an exhaustive computational study comparing TRW-S with branch-and-cut algorithms under varying wind-regime complexity and problem resolutions. We demonstrate the broad applicability of techniques we develop by solving a suite of benchmark quadratic knapsack problems, a general class of problems that arise in many settings.%%%%M.A.S

关键词： approximate inference probabilistic graphical models wind farm design 0796

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Learning Gated Bayesian Networks for Algorithmic Trading

Learning Gated Bayesian Networks for Algorithmic Trading

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7th European Workshop on probabilistic graphical models (PGM)

作者： Bendtsen, Marcus Pena, Jose M. Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden

ISBN: (纸本)9783319114330;9783319114323

Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how using the learnt GBNs can substantially lower risks towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.

关键词： probabilistic graphical models Bayesian networks algorithmic trading decision support

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Security Testbed for Preempting Attacks against Supercomputing Infrastructure

Security Testbed for Preempting Attacks against Supercomputi...

引用

2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Cao, Phuong Kalbarczyk, Zbigniew Iyer, Ravishankar K. University of Illinois at Urbana-Champaign United States National Center for Supercomputing Applications United States

ISBN: (纸本)9798350355543

Preempting attacks targeting supercomputing systems before damage remains the top security priority. The main challenge is that noisy attack attempts and unreliable alerts often mask real attacks, causing permanent damages such as system integrity violations and data breaches. This paper describes a security testbed embedded in live traffic of a supercomputer at the National Center for Supercomputing Applications (NCSA). The objective is to demonstrate attack preemption, i.e., stopping system compromise and data breaches at petascale supercomputers. Deployment of our testbed at NCSA enables the following key contributions:1)Insights from characterizing unique attack patterns found in real security logs of more than 200 security incidents curated in the past two decades at NCSA.2)Deployment of an attack visualization tool to illustrate the challenges of identifying real attacks in HPC environments and to support security operators in interactive attack analyses.3)Demonstrate the testbed's utility by running novel models, such as Factor Graph-Based models, to preempt a real-world ransomware *** results are broadly applicable to other supercomputing and high-performance computing centers. © 2024 IEEE.

关键词： Factor Graphs Network Intrusion Preemption probabilistic graphical models

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Scaling probabilistic Inference Through Message Contraction Optimization

Scaling Probabilistic Inference Through Message Contraction ...

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2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

作者： Villescas, Martin Roa Liu, Jin-Guo Wijnings, Patrick W.A. Stuijk, Sander Corporaal, Henk Eindhoven University of Technology Eindhoven Netherlands Guangzhou China

ISBN: (纸本)9798350327595

Within the realm of probabilistic graphical models, message-passing algorithms offer a powerful framework for efficient inference. When dealing with discrete variables, these algorithms essentially amount to the addition and multiplication of multidimensional arrays with labeled dimensions, known as factors. The complexity of these algorithms is dictated by the highest-dimensional factor appearing across all computations, a metric known as the induced tree width. Although state-of-the-art methods aimed at minimizing this metric have expanded the feasi-bility of exact inference, many real-world problems continue to be intractable. In this paper, we introduce a novel method for adding and multiplying factors that results in a substantial improvement in the inference performance, especially for increasingly complex models. Our approach aligns well with existing state-of-the-art methods designed to minimize the induced tree width, thereby further expanding the tractability spectrum of exact inference for more complex models. To demonstrate the efficacy of our method, we conduct a comparative evaluation against two other open-source libraries for probabilistic inference. Our approach exhibits an average speedup of 23 times for the UAI 2014 benchmark set. For the 10 most complex problems, the average speedup increases to 64 times, demonstrating its scalability. © 2023 IEEE.

关键词： Bayesian networks message-passing algorithms probabilistic graphical models probabilistic inference

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