Download or read book Efficient Inference Algorithms for Near deterministic Systems written by Shaunak Chatterjee and published by . This book was released on 2013 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis addresses the problem of performing probabilistic inference in stochastic systems where the probability mass is far from uniformly distributed among all possible outcomes. Such near-deterministic systems arise in several real-world applications. For example, in human physiology, the widely varying evolution rates of physiological variables make certain trajectories much more likely than others; in natural language, a very small fraction of all possible word sequences accounts for a disproportionately high amount of probability under a language model. In such settings, it is often possible to obtain significant computational savings by focusing on the outcomes where the probability mass is concentrated. This contrasts with existing algorithms in probabilistic inference--such as junction tree, sum product, and belief propagation algorithms--which are well-tuned to exploit conditional independence relations. The first topic addressed in this thesis is the structure of discrete-time temporal graphical models of near-deterministic stochastic processes. We show how the structure depends on the ratios between the size of the time step and the effective rates of change of the variables. We also prove that accurate approximations can often be obtained by sparse structures even for very large time steps. Besides providing an intuitive reason for causal sparsity in discrete temporal models, the sparsity also speeds up inference. The next contribution is an eigenvalue algorithm for a linear factored system (e.g., dynamic Bayesian network), where existing algorithms do not scale since the size of the system is exponential in the number of variables. Using a combination of graphical model inference algorithms and numerical methods for spectral analysis, we propose an approximate spectral algorithm which operates in the factored representation and is exponentially faster than previous algorithms. The third contribution is a temporally abstracted Viterbi (TAV) algorithm. Starting with a spatio-temporally abstracted coarse representation of the original problem, the TAV algorithm iteratively refines the search space for the Viterbi path via spatial and temporal refinements. The algorithm is guaranteed to converge to the optimal solution with the use of admissible heuristic costs in the abstract levels and is much faster than the Viterbi algorithm for near-deterministic systems. The fourth contribution is a hierarchical image/video segmentation algorithm, that shares some of the ideas used in the TAV algorithm. A supervoxel tree provides the abstraction hierarchy for this application. The algorithm starts working with the coarsest level supervoxels, and refines portions of the tree which are likely to have multiple labels. Several existing segmentation algorithms can be used to solve the energy minimization problem in each iteration, and admissible heuristic costs once again guarantee optimality. Since large contiguous patches exist in images and videos, this approach is more computationally efficient than solving the problem at the finest level of supervoxels. The final contribution is a family of Markov Chain Monte Carlo (MCMC) algorithms for near-deterministic systems when there exists an efficient algorithm to sample solutions for the corresponding deterministic problem. In such a case, a generic MCMC algorithm's performance worsens as the problem becomes more deterministic despite the existence of the efficient algorithm in the deterministic limit. MCMC algorithms designed using our methodology can bridge this gap. The computational speedups we obtain through the various new algorithms presented in this thesis show that it is indeed possible to exploit near-determinism in probabilistic systems. Near-determinism, much like conditional independence, is a potential (and promising) source of computational savings for both exact and approximate inference. It is a direction that warrants more understanding and better generalized algorithms.
Download or read book Deterministic Artificial Intelligence written by Timothy Sands and published by BoD – Books on Demand. This book was released on 2020-05-27 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book.
Download or read book Efficient Algorithms for Structured Inference and Collaborative Learning written by Abolfazl Hashemi and published by . This book was released on 2020 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive amounts of data collected by modern information systems give rise to new challenges in the fields of signal processing, machine learning, and data analysis. In contemporary large-scale datasets, there are often hidden low-dimensional structures either in the form of parsimonious representations that best fit the data or the desired unknown information itself. Identifying parsimonious representations and exploiting underlying structural constraints lead to improved inference. Furthermore, these large-scale datasets are distributed among a network of resource-constrained systems capable of exchanging information. Hence, designing accelerated and communication efficient learning and inference algorithms is of critical importance. In the first part of this dissertation, we first study the setting where the unknown parameter of interest has hidden sparsity structures. The task of reconstructing the sparse parameter can be formulated as an l0-constrained least square problem. Motivated by the need for fast and accurate sparse recovery in large-scale setting, we propose two efficient sparse reconstruction and support selection algorithms and analyze their reconstruction performance in a variety of settings. Next, we consider applications of the proposed algorithms in structured data clustering problems where the high-dimensional data is a collection of points lying on a union of low-dimensional and evolving subspaces. By exploiting sparsity to model the low-dimensional union-of-subspaces structure of the data as well as its underlying evolutionary structure, we propose a novel evolutionary subspace clustering framework and demonstrate its successful deployment in computer vision and oceanography applications. In the second part of this dissertation, we consider observation selection and information gathering algorithms in communication-constrained networked systems where we study structural properties of observation selection criteria, design efficient greedy algorithms, and analyze their performance by leveraging the framework of weak submodular optimization. In the final part of this dissertation, we study the task of learning parameters of a machine learning model in a collaborative manner over a communication-constrained network, and design an efficient communication compressing optimization algorithm that reduces the amount of communication in the network while achieving a near optimal converge rate for general nonconvex learning tasks
Download or read book ECAI 2014 written by T. Schaub and published by IOS Press. This book was released on 2014-08 with total page 1264 pages. Available in PDF, EPUB and Kindle. Book excerpt: The role of artificial intelligence (AI) applications in fields as diverse as medicine, economics, linguistics, logical analysis and industry continues to grow in scope and importance. AI has become integral to the effective functioning of much of the technical infrastructure we all now take for granted as part of our daily lives. This book presents the papers from the 21st biennial European Conference on Artificial Intelligence, ECAI 2014, held in Prague, Czech Republic, in August 2014. The ECAI conference remains Europe's principal opportunity for researchers and practitioners of Artificial Intelligence to gather and to discuss the latest trends and challenges in all subfields of AI, as well as to demonstrate innovative applications and uses of advanced AI technology. Included here are the 158 long papers and 94 short papers selected for presentation at the conference. Many of the papers cover the fields of knowledge representation, reasoning and logic as well as agent-based and multi-agent systems, machine learning, and data mining. The proceedings of PAIS 2014 and the PAIS System Demonstrations are also included in this volume, which will be of interest to all those wishing to keep abreast of the latest developments in the field of AI.
Download or read book Robotic Systems Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2020-01-03 with total page 2075 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through expanded intelligence, the use of robotics has fundamentally transformed a variety of fields, including manufacturing, aerospace, medicine, social services, and agriculture. Continued research on robotic design is critical to solving various dynamic obstacles individuals, enterprises, and humanity at large face on a daily basis. Robotic Systems: Concepts, Methodologies, Tools, and Applications is a vital reference source that delves into the current issues, methodologies, and trends relating to advanced robotic technology in the modern world. Highlighting a range of topics such as mechatronics, cybernetics, and human-computer interaction, this multi-volume book is ideally designed for robotics engineers, mechanical engineers, robotics technicians, operators, software engineers, designers, programmers, industry professionals, researchers, students, academicians, and computer practitioners seeking current research on developing innovative ideas for intelligent and autonomous robotics systems.
Download or read book Efficient Deterministic Approxmination Algorithms for Measurement set Selection in Chain Models written by Guy Shattah and published by . This book was released on 2007 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multi Agent Systems and Applications III written by Vladimir Marik and published by Springer Science & Business Media. This book was released on 2003-06-02 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Central and European Conference on Multi-Agent Systems, CEEMAS 2003, held in Prague, Czech Republic in June 2003. The 58 revised full papers presented together with 3 invited contributions were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on formal methods, social knowledge and meta-reasoning, negotiation, and policies, ontologies and languages, planning, coalitions, evolution and emergent behaviour, platforms, protocols, security, real-time and synchronization, industrial applications, e-business and virtual enterprises, and Web and mobile agents.
Download or read book Programming Languages and Systems written by Gilles Barthe and published by Springer Science & Business Media. This book was released on 2011-03-22 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 20th European Symposium on Programming, ESOP 2011, held in Saarbrücken, Germany, March 30—April 1, 2011, as part of ETAPS 2011, the European Joint Conferences on Theory and Practice of Software. The 24 revised full papers presented together with one full length invited talk were carefully reviewed and selected from 93 full paper submissions. Papers were invited on all aspects of programming language research including: programming paradigms and styles, methods and tools to write and specify programs and languages, methods and tools for reasoning about programs, methods and tools for implementation, and concurrency and distribution.
Download or read book Uncertainty in Artificial Intelligence written by MKP and published by Elsevier. This book was released on 2014-06-28 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty Proceedings 1994
Download or read book Information Theory Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Download or read book Handbook of Research on Computational Methodologies in Gene Regulatory Networks written by Das, Sanjoy and published by IGI Global. This book was released on 2009-10-31 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization"--Provided by publisher.
Download or read book Towards Autonomous Robotic Systems written by Roderich Groß and published by Springer. This book was released on 2011-08-19 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th Annual Conference Towards Autonomous Robotics Systems, TAROS 2011, held in Sheffield, UK, in August/September 2011. The 32 revised full papers presented together with 29 two-page abstracts were carefully reviewed and selected from 94 submissions. Among the topics addressed are robot navigation, robot learning, human-robot interaction, robot control, mobile robots, reinforcement learning, robot vehicles, swarm robotic systems, etc.
Download or read book Bayesian Reasoning and Machine Learning written by David Barber and published by Cambridge University Press. This book was released on 2012-02-02 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Download or read book Advances in Neural Information Processing Systems 19 written by Bernhard Schölkopf and published by MIT Press. This book was released on 2007 with total page 1668 pages. Available in PDF, EPUB and Kindle. Book excerpt: The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.
Download or read book Energy Storage in the Emerging Era of Smart Grids written by Rosario Carbone and published by BoD – Books on Demand. This book was released on 2011-09-22 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reliable, high-efficient and cost-effective energy storage systems can undoubtedly play a crucial role for a large-scale integration on power systems of the emerging "distributed generation" (DG) and for enabling the starting and the consolidation of the new era of so called smart-grids. A non exhaustive list of benefits of the energy storage properly located on modern power systems with DG could be as follows: it can increase voltage control, frequency control and stability of power systems, it can reduce outages, it can allow the reduction of spinning reserves to meet peak power demands, it can reduce congestion on the transmission and distributions grids, it can release the stored energy when energy is most needed and expensive, it can improve power quality or service reliability for customers with high value processes or critical operations and so on. The main goal of the book is to give a date overview on: (I) basic and well proven energy storage systems, (II) recent advances on technologies for improving the effectiveness of energy storage devices, (III) practical applications of energy storage, in the emerging era of smart grids.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1992 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Efficient Bayesian Inference in Non linear Switching State Space Models Using Particle Gibbs Sampling Approaches written by Jaeho Kim and published by . This book was released on 2016 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops a new Bayesian algorithm to efficiently estimate non-linear/non-Gaussian state space models with abruptly changing parameters. Within the Particle Gibbs framework developed by Andrieu et al. (2010), the proposed algorithm effectively combines two ideas: ancestor sampling and a partially deterministic sequential Monte Carlo method. In the proposed approach, the discrete latent state variable that governs the switching behavior of a complex dynamic system is deterministically generated to fully diversify particles, and ancestor sampling enables complete exploitation of the various generated particles. Without a large number of particles and sophisticated tailored importance distributions, the newly developed PG sampler is shown to be both easy to implement and computationally efficient, and it substantially outperforms a standard PG technique.
