Download or read book Gradient Expectations written by Keith L. Downing and published by MIT Press. This book was released on 2023-07-18 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI. Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world? Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simulated evolution of advanced neural network systems. Downing delves into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks and determine more precisely how the ability to predict might have evolved from more primitive neural circuits. He then surveys past and present computational neural models that leverage predictive mechanisms with biological plausibility, identifying elements, such as gradients, that natural and artificial networks share. Behind well-founded predictions lie gradients, Downing finds, but of a different scope than those that belong to today’s deep learning. Digging into the connections between predictions and gradients, and their manifestation in the brain and neural networks, is one compelling example of how Downing enriches both our understanding of such relationships and their role in strengthening AI tools. Synthesizing critical research in neuroscience, cognitive science, and connectionism, Gradient Expectations offers unique depth and breadth of perspective on predictive neural-network models, including a grasp of predictive neural circuits that enables the integration of computational models of prediction with evolutionary algorithms.

Download or read book Gradient Expectations written by Keith L. Downing and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI. Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simulated evolution of advanced neural network systems. Downing delves into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks and determine more precisely how the ability to predict might have evolved from more primitive neural circuits. He then surveys past and present computational neural models that leverage predictive mechanisms with biological plausibility, identifying elements, such as gradients, that natural and artificial networks share. Behind well-founded predictions lie gradients, Downing finds, but of a different scope than those that belong to today's deep learning. Digging into the connections between predictions and gradients, and their manifestation in the brain and neural networks, is one compelling example of how Downing enriches both our understanding of such relationships and their role in strengthening AI tools. Synthesizing critical research in neuroscience, cognitive science, and connectionism, Gradient Expectations offers unique depth and breadth of perspective on predictive neural-network models, including a grasp of predictive neural circuits that enables the integration of computational models of prediction with evolutionary algorithms.

Download or read book Inflectional Defectiveness written by Andrea D. Sims and published by Cambridge University Press. This book was released on 2015-11-12 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Paradigmatic gaps ('missing' inflected forms) have traditionally been considered to be the random detritus of a language's history and marginal exceptions to the normal functioning of its inflectional system. Arguing that this is a misperception, Inflectional Defectiveness demonstrates that paradigmatic gaps are in fact normal and expected products of inflectional structure. Sims offers an accessible exploration of how and why inflectional defectiveness arises, why it persists, and how it is learned. The book presents a theory of morphology which is rooted in the implicative structure of the paradigm. This systematic exploration of the topic also addresses questions of inflection class organization, the morphology-syntax interface, the structure of the lexicon, and the nature of productivity. Presenting a novel synthesis of established research and new empirical data, this work is significant for researchers and graduate students in all fields of linguistics.

Download or read book Post school Pathways of Migrant Origin Youth in Europe written by Merike Darmody and published by Taylor & Francis. This book was released on 2023-06-26 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume explores the role of structure and agency in shaping post-school pathways for migrant-origin young people, providing new insights from countries with different migration histories and transition systems. The book collates the work of leading international scholars to cover a number of jurisdictions across Europe, looking in depth at migrant transitions in different contexts. The chapters examine the influence of different education systems, migration status, race and ethnicity, social class, gender, and resilience on the success of transitions to higher education and the labour market. The book highlights the need for host countries to put in place comprehensive policies to counter ethnic inequalities and discrimination in their education and labour market systems while facilitating and supporting immigrant youth in pursuing their post-school pathways. This timely book will be of great interest to scholars, researchers, and postgraduate students in the fields of migration studies, sociology of education, and equity in education. Policymakers will find this book useful in informing policy development in education and the labour market.

Download or read book Case Studies in Applied Bayesian Data Science written by Kerrie L. Mengersen and published by Springer Nature. This book was released on 2020-05-28 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields. While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration.

Download or read book Family Cultural Capital and Student Achievement written by Cheng Yong Tan and published by Springer Nature. This book was released on 2020-04-09 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the relationship between cultural capital and student achievement. It fills the gap in the literature on large-scale quantitative studies of the effects of cultural capital. In particular, the review of empirical evidence presented, especially that from studies analyzing large-scale, international data from the Programme for International Student Assessment (PISA), makes a substantial contribution to the literature. This review addresses the knowledge gap on reviews investigating the effects of different forms of cultural capital on student achievement as compared to the more established evidence base in the related field of socioeconomic status.

Download or read book Computer Aided Methods in Optimal Design and Operations written by Ian David Lockhart Bogle and published by World Scientific. This book was released on 2006 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers different topics on optimal design and operations with particular emphasis on chemical engineering applications. A wide range of optimization methods OCo deterministic, stochastic, global and hybrid OCo are considered. Containing papers presented at the bilateral workshop by British and Lithuanian scientists, the book brings together researchers'' contributions from different fields OCo chemical engineering including reaction and separation processes, food and biological production, as well as business cycle optimization, bankruptcy, protein analysis and bioinformatics. Sample Chapter(s). Chapter 1: Hybrid Methods for Optimisation (520 KB). Contents: Hybrid Methods for Optimisation (E S Fraga); An MILP Model for Multi-Class Data Classification (G Xu & L G Papageorgiou); Studying the Rate of Convergence of the Steepest Descent Optimisation Algorithm with Relaxation (R J Haycroft); Optimal Estimation of Parameters in Market Research Models (V Savani); A Redundancy Detection Approach to Mining Bioinformatics Data (H Camacho & A Salhi); Optimal Open-Loop Recipe Generation for Particle Size Distribution Control in Semi-Batch Emulsion Polymerisation (N Bianco & C D Immanuel); Multidimensional Scaling Using Parallel Genetic Algorithm (A Varoneckas et al.); Evaluating the Applicability of Time Temperature Integrators as Process Exploration and Validation Tools (S Bakalis et al.); Optimal Deflection Yoke Tuning (V Vaitkus et al.); and other papers. Readership: Academics, researchers, practitioners and postgraduates students in operations research and engineering."

Download or read book Modern Trends in Controlled Stochastic Processes written by Alexey Piunovskiy and published by Springer Nature. This book was released on 2021-06-04 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported. Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented. Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference. ​

Download or read book Substance and Behavioral Addictions written by Steve Sussman and published by Cambridge University Press. This book was released on 2017-02-06 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the etiology, assessment, prevention and cessation of eleven focal addictions within an appetitive motivation framework of addiction. It is intended for upper-level undergraduates and graduate students, practitioners, and researchers who want an introduction to cutting edge research and practice in the addictions field.

Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2023-08-15 with total page 1352 pages. Available in PDF, EPUB and Kindle. Book excerpt: An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment

Download or read book Principles of Brain Dynamics written by Mikhail I. Rabinovich and published by MIT Press. This book was released on 2012-07-06 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimental and theoretical approaches to global brain dynamics that draw on the latest research in the field. The consideration of time or dynamics is fundamental for all aspects of mental activity—perception, cognition, and emotion—because the main feature of brain activity is the continuous change of the underlying brain states even in a constant environment. The application of nonlinear dynamics to the study of brain activity began to flourish in the 1990s when combined with empirical observations from modern morphological and physiological observations. This book offers perspectives on brain dynamics that draw on the latest advances in research in the field. It includes contributions from both theoreticians and experimentalists, offering an eclectic treatment of fundamental issues. Topics addressed range from experimental and computational approaches to transient brain dynamics to the free-energy principle as a global brain theory. The book concludes with a short but rigorous guide to modern nonlinear dynamics and their application to neural dynamics.

Download or read book Algorithms for Decision Making written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2022-08-16 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

Download or read book Transactions on Large Scale Data and Knowledge Centered Systems XLVIII written by Abdelkader Hameurlain and published by Springer Nature. This book was released on 2021-05-17 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 48th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains 8 invited papers dedicated to the memory of Prof. Dr. Roland Wagner. The topics covered include distributed database systems, NewSQL, scalable transaction management, strong consistency, caches, data warehouse, ETL, reinforcement learning, stochastic approximation, multi-agent systems, ontology, model-driven development, organisational modelling, digital government, new institutional economics and data governance.

Download or read book Egophoricity written by Simeon Floyd and published by John Benjamins Publishing Company. This book was released on 2018-04-15 with total page 515 pages. Available in PDF, EPUB and Kindle. Book excerpt: Egophoricity refers to the grammaticalised encoding of personal knowledge or involvement of a conscious self in a represented event or situation. Most typically, a marker that is egophoric is found with first person subjects in declarative sentences and with second person subjects in interrogative sentences. This person sensitivity reflects the fact that speakers generally know most about their own affairs, while in questions this epistemic authority typically shifts to the addressee. First described for Tibeto-Burman languages, egophoric-like patterns have now been documented in a number of other regions around the world, including languages of Western China, the Andean region of South America, the Caucasus, Papua New Guinea, and elsewhere. This book is a first attempt to place detailed descriptions of this understudied grammatical category side by side and to add to the cross-linguistic picture of how ideas of self and other are encoded and projected in language. The diverse but conceptually related egophoric phenomena described in its chapters provide fascinating case studies for how structural patterns in morphosyntax are forged under intersubjective, interactional pressures as we link elements of our speech to our speech situation.

Download or read book Encyclopedia of Ecology written by Brian D. Fath and published by Elsevier. This book was released on 2018-08-23 with total page 2786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Encyclopedia of Ecology, Second Edition, Four Volume Set continues the acclaimed work of the previous edition published in 2008. It covers all scales of biological organization, from organisms, to populations, to communities and ecosystems. Laboratory, field, simulation modelling, and theoretical approaches are presented to show how living systems sustain structure and function in space and time. New areas of focus include micro- and macro scales, molecular and genetic ecology, and global ecology (e.g., climate change, earth transformations, ecosystem services, and the food-water-energy nexus) are included. In addition, new, international experts in ecology contribute on a variety of topics. Offers the most broad-ranging and comprehensive resource available in the field of ecology Provides foundational content and suggests further reading Incorporates the expertise of over 500 outstanding investigators in the field of ecology, including top young scientists with both research and teaching experience Includes multimedia resources, such as an Interactive Map Viewer and links to a CSDMS (Community Surface Dynamics Modeling System), an open-source platform for modelers to share and link models dealing with earth system processes

Download or read book Perturbations Optimization and Statistics written by Tamir Hazan and published by MIT Press. This book was released on 2017-09-22 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.