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Sequence Learning

Sequence Learning
Author: Ron Sun
Publisher: Springer
Total Pages: 400
Release: 2003-06-29
Genre: Computers
ISBN: 354044565X

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Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.


Supervised Sequence Labelling with Recurrent Neural Networks

Supervised Sequence Labelling with Recurrent Neural Networks
Author: Alex Graves
Publisher: Springer
Total Pages: 148
Release: 2012-02-06
Genre: Technology & Engineering
ISBN: 3642247970

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Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.


Attention and Implicit Learning

Attention and Implicit Learning
Author: Luis Jiménez
Publisher: John Benjamins Publishing
Total Pages: 395
Release: 2003-01-30
Genre: Psychology
ISBN: 9027296405

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Attention and Implicit Learning provides a comprehensive overview of the research conducted in this area. The book is conceived as a multidisciplinary forum of discussion on the question of whether implicit learning may be depicted as a process that runs independently of attention. The volume also deals with the complementary question of whether implicit learning affects the dynamics of attention, and it addresses these questions from perspectives that range from functional to neuroscientific and computational approaches. The view of implicit learning that arises from these pages is not that of a mysterious faculty, but rather that of an elementary ability of the cognitive systems to extract the structure of their environment as it appears directly through experience, and regardless of any intention to do so. Implicit learning, thus, is taken to be a process that may shape not only our behavior, but also our representations of the world, our attentional functions, and even our conscious experience. (Series B)


Understanding Intuition

Understanding Intuition
Author: Lois Isenman
Publisher: Academic Press
Total Pages: 244
Release: 2018-04-12
Genre: Psychology
ISBN: 0128141093

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Understanding Intuition: A Journey In and Out of Science explores the biological and cognitive mechanisms that account for intuition, and examines the first-person experience. The book integrates both scientific and personal perspectives on this important yet elusive mental capacity. It uses specific encounters to illustrate that intuition is enhanced when we can attend to the subtle aspects of our inner experiences, such as bodily sensations, images, and differing kinds of intuitive evaluative feelings, all of which may emerge no further than on the fringe of awareness. This awareness of subtle inner experiences helps forge a more fluid exchange between the unconscious and conscious minds, and allows readers to calibrate their own intuitions. Over the course of the book, readers will gain a deeper appreciation and respect for the unconscious mind and its potential sophistication, and even its potential wisdom. Understanding Intuition is a timely and critical resource for students and researchers in psychology, cognitive science, theology, women’s studies, and neuroscience. Stresses the powerful influence of the unconscious mind and its important adaptive role Frames intuition as significant and novel unconscious insight Presents a systematic framework for understanding different kinds of intuition Examines the emotional underpinnings of intuition, giving special emphasis to the role of somatic feelings and their derivatives


Learning

Learning
Author: Angela D. Friederici
Publisher: Walter de Gruyter
Total Pages: 316
Release: 1998
Genre: Medical
ISBN: 9783110161335

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Online and Offline Modulators of Motor Learning

Online and Offline Modulators of Motor Learning
Author: Shahabeddin Vahdat
Publisher: Frontiers Media SA
Total Pages: 157
Release: 2017-05-25
Genre: Electronic book
ISBN: 2889451666

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Both the acquisition of new and the modification of previously acquired motor skills are necessary to achieve optimal levels of motor performance in everyday functioning as well as to attain expert performance levels that are evident in sports and arts. A multitude of factors have been shown to influence the various stages of the learning process, from the acquisition (i.e., motor memory encoding) to the consolidation and subsequent retention of a skill. These factors, or modulators, can affect learning through online processes taking place during practice of a new motor skill or through offline processes occurring in the absence of task performance (i.e., after training sessions). Although much of the recent research from various disciplines has placed an increased emphasis on identifying factors that can influence the motor learning process, we lack an integrated understanding of online and offline determinants of motor skill behaviours. Potential motor learning modulators include, but are certainly not limited to, stress, anxiety, attention, executive functioning, social interaction, stimulus-response mapping, training schedule/regimen, learning environment, vigilance/consciousness states including sleep, wakefulness or meditation, brain stimulation, interference as well as resting state brain connectivity. Pathological and non-pathological (i.e., development or aging) changes in the brain can also be conceptualized as potential modulators. The aim of this Research Topic is to bridge research from the cognitive, sensory, motor and psychological domains using various behavioural paradigms and neuroimaging techniques in order to provide a comprehensive view of the online and offline modulators of motor learning, and how they interact to influence motor performance. Critically, the overarching goal is to gain a better understanding of how motor behaviour can be optimized. We believe that merging research from diverse neuroscientific communities would contribute to fulfilling this goal and potentially highlight possible shared neurophysiological mechanisms influencing motor learning.


Concise Learning and Memory

Concise Learning and Memory
Author:
Publisher: Academic Press
Total Pages: 888
Release: 2010-05-25
Genre: Psychology
ISBN: 9780080877860

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The study of learning and memory is a central topic in neuroscience and psychology. Many of the basic research findings are directly applicable in the treatment of diseases and aging phenomena, and have found their way into educational theory and praxis. Concise Learning and Memory represents the best 30 chapters from Learning and Memory: A comprehensive reference (Academic Press March 2008), the most comprehensive source of information about learning and memory ever assembled, selected by one of the most respective scientists in the field, John H. Byrne. This concise version provides a truly authoritative collection of overview articles representing fundamental reviews of our knowledge of this central cognitive function of animal brains. It will be an affordable and accessible reference for scientists and students in all areas of neuroscience and psychology. There is no other single-volume reference with such authority and comprehensive coverage and depth currently available. * Represents an authoritative selection of the fundamental chapters from the most comprehensive source of information about learning and memory ever assembled, Learning and Memory - A comprehensive reference (Academic Press Mar 2008) * Representing outstanding scholarship, each chapter is written by a leader in the field and an expert in the topic area * All topics represent the most up to date research * Full color throughout, heavily illustrated * Priced to provide an affordable reference to individuals and workgroups


Deep Learning and its Applications using Python

Deep Learning and its Applications using Python
Author: Niha Kamal Basha
Publisher: John Wiley & Sons
Total Pages: 227
Release: 2023-09-27
Genre: Computers
ISBN: 1394167776

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This book thoroughly explains deep learning models and how to use Python programming to implement them in applications such as NLP, face detection, face recognition, face analysis, and virtual assistance (chatbot, machine translation, etc.). It provides hands-on guidance in using Python for implementing deep learning application models. It also identifies future research directions for deep learning.


Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence

Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence
Author: Gogate, Lakshmi
Publisher: IGI Global
Total Pages: 451
Release: 2013-02-28
Genre: Computers
ISBN: 1466629746

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The process of learning words and languages may seem like an instinctual trait, inherent to nearly all humans from a young age. However, a vast range of complex research and information exists in detailing the complexities of the process of word learning. Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence strives to combine cross-disciplinary research into one comprehensive volume to help readers gain a fuller understanding of the developmental processes and influences that makeup the progression of word learning. Blending together developmental psychology and artificial intelligence, this publication is intended for researchers, practitioners, and educators who are interested in language learning and its development as well as computational models formed from these specific areas of research.