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2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Hasani, Behzad Arzani, Mohammad M. Fathy, Mahmood Raahemifar, Kaamran Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Ryerson Univ Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781509058204

Discriminating probabilistic graphical models are reliable tools for a sequence labeling task. Conditional Random Fields (CRFs) are discriminative models which will enable us to label a sequence of input data. Other variations of CRFs have been proposed. Hidden Conditional Random Fields (HCRFs) incorporate hidden states to the CRF model and assign a label for the whole input sequence as the model's output. Latent-Dynamic Conditional Random Fields (LDCRFs) also incorporate hidden variable states to the CRFs, in addition, these models are able to label each output variables separately. These models can capture subtle changes among different classes which will help us to achieve better recognition results. In this work we experiment various models and settings in order to achieve better results in facial expression recognition from sequence of videos. We use CRF and LDCRF models and train them with Limited-memory BFGS and Conjugate Gradient parameter learning methods. For each model we use various feature vectors in order to achieve better recognition results. We use Active Appearance Model (AAM) landmark points, Histogram of Oriented Gradients (HOG) and Uniform Local Binary Pattern (U-LBP) as our feature vectors in our models. We show which combination of learning methods and feature vectors are suitable for CRF and LDCRF discriminative models.

关键词： probabilistic graphical models Discriminative models Pattern Local Binary Active Appearance models Facial Expression

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Learning Orthographic Structure With Sequential Generative Neural Networks

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COGNITIVE SCIENCE 2016年第3期40卷 579-606页

作者： Testolin, Alberto Stoianov, Ivilin Sperduti, Alessandro Zorzi, Marco Univ Padua Dept Dev Psychol & Socialisat I-35131 Padua Italy Univ Padua Dept Gen Psychol Via Venezia 12 I-35131 Padua Italy CNRS Cognit Psychol Lab F-75700 Paris France Aix Marseille Univ Marseille France Univ Padua Dept Math I-35131 Padua Italy Univ Padua Ctr Cognit Neurosci I-35131 Padua Italy IRCCS San Camillo Neurorehabilitat Hosp Turin Italy

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain.

关键词： Connectionist modeling Recurrent neural networks Restricted Boltzmann machines probabilistic graphical models Generative models Unsupervised learning Statistical sequence learning Orthographic structure

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Wind Farm Layout Optimization Using Approximate Inference in graphical models

Wind Farm Layout Optimization Using Approximate Inference in...

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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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A probabilistic graphical Model-based Approach for Minimizing Energy Under Performance Constraints 15

A Probabilistic Graphical Model-based Approach for Minimizin...

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20th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Mishra, Nikita Zhang, Huazhe Lafferty, John D. Hoffmann, Henry Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9781450328357

In many deployments, computer systems are underutilized - meaning that applications have performance requirements that demand less than full system capacity. Ideally, we would take advantage of this under-utilization by allocating system resources so that the performance requirements are met and energy is minimized. This optimization problem is complicated by the fact that the performance and power consumption of various system configurations are often application or even input - dependent. Thus, practically, minimizing energy for a performance constraint requires fast, accurate estimations of application-dependent performance and power tradeoffs. This paper investigates machine learning techniques that enable energy savings by learning Pareto-optimal power and performance tradeoffs. Specifically, we propose LEO, a probabilistic graphical model-based learning system that provides accurate online estimates of an application's power and performance as a function of system configuration. We compare LEO to (1) offline learning, (2) online learning, (3) a heuristic approach, and (4) the true optimal solution. We find that LEO produces the most accurate estimates and near optimal energy savings.

关键词： Design Experimentation Measurement Performance Adaptation Dynamic systems Statistics probabilistic graphical models Energy minimization

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Automatic Images Annotation Extension Using a probabilistic graphical Model 16th

Automatic Images Annotation Extension Using a Probabilistic ...

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16th International Conference on Computer Analysis of Images and Patterns (CAIP)

作者： Bouzaieni, Abdessalem Tabbone, Salvatore Barrat, Sabine Univ Lorraine LORIA XILOPIX Nancy France Univ Lorraine LORIA Nancy France Univ Tours Comp Sci Lab Tours France

ISBN: (纸本)9783319231174;9783319231167

With the fast development of digital cameras and social media image sharing, automatic image annotation has become a research area of great interest. It enables indexing, extracting and searching in large collections of images in an easier and faster way. In this paper, we propose a model for the annotation extension of images using a probabilistic graphical model. This model is based on a mixture of multinomial distributions and mixtures of Gaussians. The results of the proposed model are promising on three standard datasets: Corel-5k, ESP-Game and IAPRTC-12.

关键词： Image annotation Annotation extension probabilistic graphical models Gaussian mixtures Visual characteristics Textual characteristics

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A hierarchical Bayesian-MAP approach to inverse problems in imaging

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INVERSE PROBLEMS 2016年第7期32卷 075003-075003页

作者： Raj, Raghu G. US Naval Res Lab Washington DC 20375 USA

We present a novel approach to inverse problems in imaging based on a hierarchical Bayesian-MAP (HB-MAP) formulation. In this paper we specifically focus on the difficult and basic inverse problem of multi-sensor (tomographic) imaging wherein the source object of interest is viewed from multiple directions by independent sensors. Given the measurements recorded by these sensors, the problem is to reconstruct the image (of the object) with a high degree of fidelity. We employ a probabilistic graphical modeling extension of the compound Gaussian distribution as a global image prior into a hierarchical Bayesian inference procedure. Since the prior employed by our HB-MAP algorithm is general enough to subsume a wide class of priors including those typically employed in compressive sensing (CS) algorithms, HB-MAP algorithm offers a vehicle to extend the capabilities of current CS algorithms to include truly global priors. After rigorously deriving the regression algorithm for solving our inverse problem from first principles, we demonstrate the performance of the HB-MAP algorithm on Monte Carlo trials and on real empirical data (natural scenes). In all cases we find that our algorithm outperforms previous approaches in the literature including filtered back-projection and a variety of state-of-the-art CS algorithms. We conclude with directions of future research emanating from this work.

关键词： inverse problems imaging compressive sensing Bayesian probabilistic graphical models hierarchical Bayes sparse reconstruction

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Using Binary Trees for the Evaluation of Influence Diagrams

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2016年第1期24卷 59-89页

作者： Cabanas, Rafael Gomez-Olmedo, Manuel Cano, Andres Univ Granada CITIC UGR Dept Comp Sci & Artificial Intelligence C Daniel Saucedo Aranda S-N E-18071 Granada Spain

This paper proposes the use of binary trees for representing and managing the potentials involved in Influence Diagrams. This kind of tree allows representing context-specific independencies that are finer-grained compared to those encoded using other representations. This enhanced capability can be used to improve the efficiency of the inference algorithms used for Influence Diagrams. Moreover, binary trees allow computing approximate solutions when exact inference is not feasible. In this work we describe how binary trees can be used to perform this approximate evaluation and we compare them with other structures present in the literature.

关键词： probabilistic graphical models influence diagrams approximate computation variable elimination context-specific independencies

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Asynchronous decoding of finger movements from ECoG signals using long-range dependencies conditional random fields

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JOURNAL OF NEURAL ENGINEERING 2016年第3期13卷 036017-036017页

作者： Delgado Saa, Jaime F. De Pesters, Adriana Cetin, Mujdat Univ Norte Biomed Signal Proc & Artificial Intelligence Lab Barranquilla Colombia Natl Ctr Adapt Neurotechnol Schalk Lab Albany NY USA Sabanci Univ Signal Proc & Informat Syst Lab Istanbul Turkey

Objective. In this work we propose the use of conditional random fields with long-range dependencies for the classification of finger movements from electrocorticographic recordings. Approach. The proposed method uses long-range dependencies taking into consideration time-lags between the brain activity and the execution of the motor task. In addition, the proposed method models the dynamics of the task executed by the subject and uses information about these dynamics as prior information during the classification stage. Main results. The results show that incorporating temporal information about the executed task as well as incorporating long-range dependencies between the brain signals and the labels effectively increases the system's classification performance compared to methods in the state of art. Significance. The method proposed in this work makes use of probabilistic graphical models to incorporate temporal information in the classification of finger movements from electrocorticographic recordings. The proposed method highlights the importance of including prior information about the task that the subjects execute. As the results show, the combination of these two features effectively produce a significant improvement of the system's classification performance.

关键词： Brain-computer interfaces ECoG synchronous classification temporal dynamics probabilistic graphical models

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Optimal sequence of tests for the mediastinal staging of non-small cell lung cancer

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2016年第1期16卷 9-9页

作者： Luque, Manuel Javier Diez, Francisco Disdier, Carlos UNED Dept Artificial Intelligence Juan del Rosal 16 Madrid 28040 Spain Univ Hosp Dept Pulm CIBERES CIBER Resp Dis Ramon & Cajal 3 Valencia 47005 Spain

Background: Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer and the most difficult to predict. When there are no distant metastases, the optimal therapy depends mainly on whether there are malignant lymph nodes in the mediastinum. Given the vigorous debate among specialists about which tests should be used, our goal was to determine the optimal sequence of tests for each patient. Methods: We have built an influence diagram (ID) that represents the possible tests, their costs, and their outcomes. This model is equivalent to a decision tree containing millions of branches. In the first evaluation, we only took into account the clinical outcomes (effectiveness). In the second, we used a willingness-to-pay of (sic) 30,000 per quality adjusted life year (QALY) to convert economic costs into effectiveness. We assigned a second-order probability distribution to each parameter in order to conduct several types of sensitivity analysis. Results: Two strategies were obtained using two different criteria. When considering only effectiveness, a positive computed tomography (CT) scan must be followed by a transbronchial needle aspiration (TBNA), an endobronchial ultrasound (EBUS), and an endoscopic ultrasound (EUS). When the CT scan is negative, a positron emission tomography (PET), EBUS, and EUS are performed. If the TBNA or the PET is positive, then a mediastinoscopy is performed only if the EBUS and EUS are negative. If the TBNA or the PET is negative, then a mediastinoscopy is performed only if the EBUS and the EUS give contradictory results. When taking into account economic costs, a positive CT scan is followed by a TBNA;an EBUS is done only when the CT scan or the TBNA is negative. This recommendation of performing a TBNA in certain cases should be discussed by the pneumology community because TBNA is a cheap technique that could avoid an EBUS, an expensive test, for many patients. Conclusions: We have determined the optimal sequence of tests f

关键词： Decision making under uncertainty Cost-effectiveness analysis in medicine probabilistic graphical models Influence diagrams Bayesian networks

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Hinge-loss Markov random fields and probabilistic soft logic

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Stephen H. Bach Matthias Broecheler Bert Huang Lise Getoor Computer Science Department Stanford University Stanford CA DataStax Computer Science Department Virginia Tech Blacksburg VA Computer Science Department University of California Santa Cruz Santa Cruz CA

A fundamental challenge in developing high-impact machine learning technologies is balancing the need to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge graphs and the Web, to images, video, and natural language. In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data. The first, hingeloss Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model that generalizes different approaches to convex inference. We unite three approaches from the randomized algorithms, probabilistic graphical models, and fuzzy logic communities, showing that all three lead to the same inference objective. We then define HL-MRFs by generalizing this unified objective. The second new formalism, probabilistic soft logic (PSL), is a probabilistic programming language that makes HL-MRFs easy to define using a syntax based on first-order logic. We introduce an algorithm for inferring most-probable variable assignments (MAP inference) that is much more scalable than general-purpose convex optimization methods, because it uses message passing to take advantage of sparse dependency structures. We then show how to learn the parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous discrete models, but much more scalable. Together, these algorithms enable HL-MRFs and PSL to model rich, structured data at scales not previously possible.

关键词： probabilistic graphical models statistical relational learning structured prediction

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