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Hardware Accelerator for probabilistic Inference in 65-nm CMOS

IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2016年第3期24卷 837-845页

作者： Khan, Osama U. Wentzloff, David D. Univ Calif Berkeley Dept Elect Engn Berkeley CA 94720 USA Univ Michigan Dept Elect Engn Ann Arbor MI 48109 USA

A hardware accelerator is presented to compute the probabilistic inference for a Bayesian network (BN) in distributed sensing applications. For energy efficiency, the accelerator is operated at a near-threshold voltage of 0.5 V, while achieving a maximum clock frequency of 33 MHz. Clique-tree message passing algorithm is leveraged to compute the probabilistic inference. The theoretical maximum size of a factor that the proposed hardware accelerator can handle is 2((8x20)=160) entries, which is sufficient for handling massive BNs, such as PATHFINDER, MUNIN, and so on (>1000 nodes). A Logical Alarm Reduction Mechanism (ALARM) BN is used to benchmark the performance of the accelerator. The accelerator consumes 76 nJ to execute the ALARM network using a clique-tree message-passing algorithm, while the same algorithm executed on an ultralow-power microcontroller consumes 20 mJ.

关键词： Bayesian network (BN) clique-tree embedded machine learning hardware accelerator intelligent sensor node message passing probabilistic graphical model probabilistic inference

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Joint model of topics, expertises, activities and trends for question answering Web applications

Joint model of topics, expertises, activities and trends for...

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Meng, Zide Gandon, Fabien Zucker, Catherine Faron INRIA SAM Sophia Antipolis France Univ Nice Sophia Antipolis Nice France

ISBN: (纸本)9781509044702

Users in question-answer sites generate huge amounts of high quality and highly reusable information. This information can be categorized by topics but since users' interests change with time, uncovering the temporal patterns and trends in their activity is of prime interest to detect their current expertize. These temporal variations have long remained unexplored in question-answer sites while detecting them enables us to improve tasks such as: question routing, expert recommending and community life-cycle management. In this paper, we propose a generative model of such a community and its dynamics, and we perform experiments with real-world data extracted from the StackOverflow website to confirm the effectiveness of our model to study the users' behaviors and topics dynamics.

关键词： Community Question Answering probabilistic graphical model Question routing Temporal analysis Topic modeling

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Facial Landmark Detection and Tracking for Facial Behavior Analysis 16

Facial Landmark Detection and Tracking for Facial Behavior A...

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ACM International Conference on Multimedia Retrieval (ICMR)

作者： Wu, Yue Rensselaer Polytech Inst 110 8th St Troy NY 12180 USA

ISBN: (纸本)9781450343596

The face is the most dominant and distinct communication tool of human beings. Automatic analysis of facial behavior allows machines to understand and interpret a human's states and needs for natural interactions. This research focuses on developing advanced computer vision techniques to process and analyze facial images for the recognition of various facial behaviors. Specifically, this research consists of two parts: automatic facial landmark detection and tracking, and facial behavior analysis and recognition using the tracked facial landmark points. In the first part, we develop several facial landmark detection and tracking algorithms on facial images with varying conditions, such as varying facial expressions, head poses and facial occlusions. First, to handle facial expression and head pose variations, we introduce a hierarchical probabilistic face shape model and a discriminative deep face shape model to capture the spatial relationships among facial landmark points under different facial expressions and face poses to improve facial landmark detection. Second, to handle facial occlusion, we improve upon the effective cascade regression framework and propose the robust cascade regression framework for facial landmark detection, which iteratively predicts the landmark visibility probabilities and landmark locations. The second part of this research applies our facial landmark detection and tracking algorithms to facial behavior analysis, including facial action recognition and face pose estimation. For facial action recognition, we introduce a novel regression framework for joint facial landmark detection and facial action recognition. For head pose estimation, we are working on a robust algorithm that can perform head pose estimation under facial occlusion.

关键词： Facial behavior analysis Facial landmark detection and tracking probabilistic graphical model

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FAQ: Questions Asked Frequently 16

FAQ: Questions Asked Frequently

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35th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS)

作者： Khamis, Mahmoud Abo Ngo, Hung Q. Rudra, Atri LogicBlox Inc New York NY 10011 USA SUNY Buffalo Buffalo NY 14260 USA SUNY Buffalo Comp Sci & Engn Buffalo NY USA

ISBN: (纸本)9781450341912

We define and study the Functional A ggregate Query (FAQ) problem, which encompasses many frequently asked questions in constraint satisfaction, databases, matrix operations, probabilistic graphical models and logic. This is our main conceptual contribution. We then present a simple algorithm called InsideOut to solve this general problem. InsideOut is a variation of the traditional dynamic programming approach for constraint programming based on variable elimination. Our variation adds a couple of simple twists to basic variable elimination in order to deal with the generality of FAQ, to take full advantage of Grohe and Marx's fractional edge cover framework, and of the analysis of recent worst-case optimal relational join algorithms. As is the case with constraint programming and graphical model inference, to make InsideOut run efficiently we need to solve an optimization problem to compute an appropriate variable ordering. The main technical contribution of this work is a precise characterization of when a variable ordering is 'semantically equivalent' to the variable ordering given by the input FAQ expression. Then, we design an approximation algorithm to find an equivalent variable ordering that has the best 'fractional FAQ-width'. Our results imply a host of known and a few new results in graphical model inference, matrix operations, relational joins, and logic. We also briefly explain how recent algorithms on beyond worst-case analysis for joins and those for solving SAT and #SAT can be viewed as variable elimination to solve FAQ over compactly represented input functions.

关键词： Join algorithm Quantified conjunctive query Logic Semiring probabilistic graphical model Constraint satisfaction

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Proposing the deep dynamic Bayesian network as a future computer based medical system 29

Proposing the deep dynamic Bayesian network as a future comp...

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29th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

作者： Carbery, Caoimhe M. Marshall, Adele H. Woods, Roger Queens Univ Belfast Ctr Stat Sci & Operat Res CenSSOR Belfast BT7 1NN Antrim North Ireland Queens Univ Belfast Inst Elect Commun & Informat Technol ECIT Belfast BT7 1NN Antrim North Ireland

ISBN: (纸本)9781467390361

The development of new learning models has been of great importance throughout recent years;with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

关键词： probabilistic graphical model deep learning dynamic Bayesian network medical systems

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A Nonparametric model for Event Discovery in the Geospatial-Temporal Space 16

A Nonparametric Model for Event Discovery in the Geospatial-...

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25th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Guo, Jinjin Gong, Zhiguo Univ Macau Dept Comp & Informat Sci Zhuhai Macau SAR Peoples R China

ISBN: (纸本)9781450340731

The availability of geographical and temporal tagged documents enables many location and time based mining tasks. Event discovery is one of such tasks, which is to identify interesting happenings in the geographical and temporal space. In recent years, several techniques have been proposed. However, no existing work has provided a nonparametric algorithm for detecting events in the joint space crossing geographical and temporal dimensions. Furthermore, though some prior works proposed to capture the periodicities of topics in their solutions, some restrictions on the temporal patterns are often placed and they usually ignore the spatial patterns of the topics. To break through such limitations, in this paper we propose a novel nonparametric model to identify events in the geographical and temporal space, where any recurrent patterns of events can be automatically captured. In our approach, parameters are automatically determined by exploiting a Dirichlet Process. To reduce the influence from noisy terms in the detection, we distinguish its event role from its background role using a Bernoulli model in the solution. Experimental results on three real world datasets show the proposed algorithm outperforms previous state-of-the-art approaches.

关键词： Event Discovery Geographical-Temporal Space probabilistic graphical model

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Simulator Prototyping Through graphical Dependency modeling 12th

Simulator Prototyping Through Graphical Dependency Modeling

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12th International Conference on Machine Learning and Data Mining (MLDM)

作者： Dheenadayalan, Kumar Muralidhara, V. N. Srinivasaraghavan, G. Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9783319419206;9783319419190

Given a complex system, simulating the behavior of the system under various conditions and inputs is a common requirement in different domains. Designing a simulator by non-domain expert is not an easy task. However, the vast amount of performance related parameters that can be monitored on any physical system can help in probabilistic modeling of the system. Accurate modeling of a complex system requires the identification of dependency among the individual components. These dependencies can be deduced by probabilistic analysis of the performance parameters generated by the physical system. We propose constrained Hill Climbing for structure learning of a Bayesian Network to learn the dependency among internal components of the system. Dependency graph, thus generated will act as a basis for developing a prototype simulator. Multivariate Adaptive Regression Splines are compared with the proposed constrained hill climbing to deduce the mathematical relation between the interdependent components. Two prototype simulators are designed, one for a complex large-scale storage system and another for a group of computer servers. Behavior of both the physical systems is tested on workload traces. The normalized mean absolute error for simulation of storage systems and server was 4.6% & 3.75% respectively. Results indicate that the proposed simulation prototyping can be a useful and unique way of understanding complex systems without the help of a domain expert.

关键词： probabilistic graphical model Bayesian network Regression Multivariate adaptive regression splines Simulator

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Aggregating Crowd Wisdom with Instance Grouping Methods 18th

Aggregating Crowd Wisdom with Instance Grouping Methods

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18th Asia-Pacific Web Conference (APWeb)

作者： Yin, Li'ang Li, Zhengbo Han, Jianhua Yu, Yong Shanghai Jiao Tong Univ Dept Comp Sci 800 Dongchuan Rd Shanghai Peoples R China

ISBN: (纸本)9783319458137;9783319458144

With the blooming of crowdsourcing platforms, utilizing crowd becomes popular. Label aggregation is one of the key topics in crowdsourcing research. The goal is to infer true labels from multiple labels provided by different users. Most researchers make their efforts in modeling user ability and instance difficulty. However, these methods may suffer from sparsity of labels in practice. In this paper, we consider label aggregation from the view of grouping instances. We assume instances are sampled from latent groups and instances in the same group share the same true label. A probabilistic graphical model named InGroup (Instance Grouping model) is constructed to infer latent group assignments as well as true labels. Further, we combine user ability and group difficulty into InGroup to achieve a better model called InGroup+ (InGroup Plus). The experiments conducted on a real-world dataset show the advantages of instance grouping methods compared with other methods.

关键词： Crowd wisdom Label aggregation Instance grouping probabilistic graphical model

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A Bayesian model of the Aggregate Interference Power in Cognitive Radio Networks 7

A Bayesian Model of the Aggregate Interference Power in Cogn...

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7th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (IEEE UEMCON)

作者： Manesh, Mohsen Riahi Kaabouch, Naima Reyes, Hector Hu, Wen-Chen Univ North Dakota Dept Elect Engn Grand Forks ND 58202 USA Univ Llanos Fac Ciencias Basicas Ingn Villavicencio Colombia Univ North Dakota Dept Comp Sci Grand Forks ND 58202 USA

ISBN: (纸本)9781509014965

Interference is one of the critical factors that affects the performance of cognitive radio networks. In these networks, secondary users are allowed to use the primary user channel with the condition that they cause no interference to it. Interference power received at the primary user is impacted by a number of parameters, including nodes transmit powers, distance between the primary user and the other nodes, path loss, and shadowing. A number of techniques have been proposed to model the interference power. However, these techniques do not consider the uncertainty associated with these parameters. Therefore, a model that deals with the uncertainty affecting the aggregate interference power is needed. In this paper, a Bayesian model is proposed to probabilistically describe how a number of factors affect the aggregate power of interference.

关键词： interference cognitive radio Bayesian network probabilistic graphical model

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iGlasses: A Novel Recommendation System for Best-fit Glasses 16

iGlasses: A Novel Recommendation System for Best-fit Glasses

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39th International ACM SIGIR conference on Research and Development in Information Retrieval

作者： Gu, Xiaoling Shou, Lidan Peng, Pai Chen, Ke Wu, Sai Chen, Gang Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450340694

We demonstrate iGlasses, a novel recommendation system that accepts a frontal face photo as the input and returns the best-fit eyeglasses as the output. As conventional recommendation techniques such as collaborative filtering become inapplicable in the problem, we propose a new recommendation method which exploits the implicit matching rules between human faces and eyeglasses. We first define fine-grained attributes for human faces and frames of glasses respectively. Then, we develop a recommendation framework based on a probabilistic graphical model, which effectively captures the correlation among these fine-grained attributes. Ranking of the frames (glasses) is done by their similarity to the query facial attributes. Finally, we produce a synthesized image for the input face to demonstrate the visual effect when wearing the recommended glasses.

关键词： Eyeglasses Recommendation probabilistic graphical model

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