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Reduced Representations Of Neural Networks
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Book Synopsis Holographic Reduced Representation by : Tony A. Plate
Download or read book Holographic Reduced Representation written by Tony A. Plate and published by Stanford Univ Center for the Study. This book was released on 2003 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: While neuroscientists garner success in identifying brain regions and in analyzing individual neurons, ground is still being broken at the intermediate scale of understanding how neurons combine to encode information. This book proposes a method of representing information in a computer that would be suited for modeling the brain's methods of processing information. Holographic Reduced Representations (HRRs) are introduced here to model how the brain distributes each piece of information among thousands of neurons. It had been previously thought that the grammatical structure of a language cannot be encoded practically in a distributed representation, but HRRs can overcome the problems of earlier proposals. Thus this work has implications for psychology, neuroscience, linguistics, and computer science, and engineering.
Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal
Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze
Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Book Synopsis Models of Neural Networks III by : Eytan Domany
Download or read book Models of Neural Networks III written by Eytan Domany and published by Springer Science & Business Media. This book was released on 1996 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a collection of articles by leading researchers in neural networks. This work focuses on data storage and retrieval, and the recognition of handwriting.
Book Synopsis ARTIFICIAL NEURAL NETWORKS by : Dr. N.N. Praboo
Download or read book ARTIFICIAL NEURAL NETWORKS written by Dr. N.N. Praboo and published by Archers & Elevators Publishing House. This book was released on with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Graph Representation Learning by : William L. William L. Hamilton
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Book Synopsis Artificial Neural Networks, 2 by : I. Aleksander
Download or read book Artificial Neural Networks, 2 written by I. Aleksander and published by Elsevier. This book was released on 2014-06-28 with total page 879 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume proceedings compilation is a selection of research papers presented at the ICANN-92. The scope of the volumes is interdisciplinary, ranging from the minutiae of VLSI hardware, to new discoveries in neurobiology, through to the workings of the human mind. USA and European research is well represented, including not only new thoughts from old masters but also a large number of first-time authors who are ensuring the continued development of the field.
Book Synopsis Neural Network Design by : Martin T. Hagan
Download or read book Neural Network Design written by Martin T. Hagan and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Theoretical Neuroscience by : Peter Dayan
Download or read book Theoretical Neuroscience written by Peter Dayan and published by MIT Press. This book was released on 2005-08-12 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Book Synopsis The Wiley Handbook of Cognitive Control by : Tobias Egner
Download or read book The Wiley Handbook of Cognitive Control written by Tobias Egner and published by John Wiley & Sons. This book was released on 2017-01-11 with total page 1070 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering basic theory, new research, and intersections with adjacent fields, this is the first comprehensive reference work on cognitive control – our ability to use internal goals to guide thought and behavior. Draws together expert perspectives from a range of disciplines, including cognitive psychology, neuropsychology, neuroscience, cognitive science, and neurology Covers behavioral phenomena of cognitive control, neuroanatomical and computational models of frontal lobe function, and the interface between cognitive control and other mental processes Explores the ways in which cognitive control research can inform and enhance our understanding of brain development and neurological and psychiatric conditions
Book Synopsis Inference on the Low Level by : Hannes Leitgeb
Download or read book Inference on the Low Level written by Hannes Leitgeb and published by Springer Science & Business Media. This book was released on 2012-11-02 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: In contrast to the prevailing tradition in epistemology, the focus in this book is on low-level inferences, i.e., those inferences that we are usually not consciously aware of and that we share with the cat nearby which infers that the bird which she sees picking grains from the dirt, is able to fly. Presumably, such inferences are not generated by explicit logical reasoning, but logical methods can be used to describe and analyze such inferences. Part 1 gives a purely system-theoretic explication of belief and inference. Part 2 adds a reliabilist theory of justification for inference, with a qualitative notion of reliability being employed. Part 3 recalls and extends various systems of deductive and nonmonotonic logic and thereby explains the semantics of absolute and high reliability. In Part 4 it is proven that qualitative neural networks are able to draw justified deductive and nonmonotonic inferences on the basis of distributed representations. This is derived from a soundness/completeness theorem with regard to cognitive semantics of nonmonotonic reasoning. The appendix extends the theory both logically and ontologically, and relates it to A. Goldman's reliability account of justified belief.
Author :Christoph von der Malsburg Publisher :Springer Science & Business Media ISBN 13 :9783540615101 Total Pages :956 pages Book Rating :4.6/5 (151 download)
Book Synopsis Artificial Neural Networks - ICANN 96 by : Christoph von der Malsburg
Download or read book Artificial Neural Networks - ICANN 96 written by Christoph von der Malsburg and published by Springer Science & Business Media. This book was released on 1996-07-10 with total page 956 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the sixth International Conference on Artificial Neural Networks - ICANN 96, held in Bochum, Germany in July 1996. The 145 papers included were carefully selected from numerous submissions on the basis of at least three reviews; also included are abstracts of the six invited plenary talks. All in all, the set of papers presented reflects the state of the art in the field of ANNs. Among the topics and areas covered are a broad spectrum of theoretical aspects, applications in various fields, sensory processing, cognitive science and AI, implementations, and neurobiology.
Book Synopsis Representations in Mind and World by : Jeffrey M. Zacks
Download or read book Representations in Mind and World written by Jeffrey M. Zacks and published by Psychology Press. This book was released on 2017-07-20 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume pulls together interdisciplinary research on cognitive representations in the mind and in the world. The chapters—from cutting-edge researchers in psychology, philosophy, computer science, and the arts—explore how structured representations determine cognition in memory, spatial cognition information visualization, event comprehension, and gesture. It will appeal to graduate-level cognitive scientists, technologists, philosophers, linguists, and educators.
Book Synopsis The Handbook of Brain Theory and Neural Networks by : Michael A. Arbib
Download or read book The Handbook of Brain Theory and Neural Networks written by Michael A. Arbib and published by MIT Press. This book was released on 2003 with total page 1328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).
Book Synopsis Hands-On Neural Networks with Keras by : Niloy Purkait
Download or read book Hands-On Neural Networks with Keras written by Niloy Purkait and published by Packt Publishing Ltd. This book was released on 2019-03-30 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your one-stop guide to learning and implementing artificial neural networks with Keras effectively Key FeaturesDesign and create neural network architectures on different domains using KerasIntegrate neural network models in your applications using this highly practical guideGet ready for the future of neural networks through transfer learning and predicting multi network modelsBook Description Neural networks are used to solve a wide range of problems in different areas of AI and deep learning. Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks. By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization. What you will learnUnderstand the fundamental nature and workflow of predictive data modelingExplore how different types of visual and linguistic signals are processed by neural networksDive into the mathematical and statistical ideas behind how networks learn from dataDesign and implement various neural networks such as CNNs, LSTMs, and GANsUse different architectures to tackle cognitive tasks and embed intelligence in systemsLearn how to generate synthetic data and use augmentation strategies to improve your modelsStay on top of the latest academic and commercial developments in the field of AIWho this book is for This book is for machine learning practitioners, deep learning researchers and AI enthusiasts who are looking to get well versed with different neural network architecture using Keras. Working knowledge of Python programming language is mandatory.
Book Synopsis Toward Human-Level Artificial Intelligence by : Eitan Michael Azoff
Download or read book Toward Human-Level Artificial Intelligence written by Eitan Michael Azoff and published by CRC Press. This book was released on 2024-09-18 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Is a computer simulation of a brain sufficient to make it intelligent? Do you need consciousness to have intelligence? Do you need to be alive to have consciousness? This book has a dual purpose. First, it provides a multi-disciplinary research survey across all branches of neuroscience and AI research that relate to this book’s mission of bringing AI research closer to building a human-level AI (HLAI) system. It provides an encapsulation of key ideas and concepts, and provides all the references for the reader to delve deeper; much of the survey coverage is of recent pioneering research. Second, the final part of this book brings together key concepts from the survey and makes suggestions for building HLAI. This book provides accessible explanations of numerous key concepts from neuroscience and artificial intelligence research, including: The focus on visual processing and thinking and the possible role of brain lateralization toward visual thinking and intelligence. Diffuse decision making by ensembles of neurons. The inside-out model to give HLAI an inner "life" and the possible role for cognitive architecture implementing the scientific method through the plan-do-check-act cycle within that model (learning to learn). A neuromodulation feature such as a machine equivalent of dopamine that reinforces learning. The embodied HLAI machine, a neurorobot, that interacts with the physical world as it learns. This book concludes by explaining the hypothesis that computer simulation is sufficient to take AI research further toward HLAI and that the scientific method is our means to enable that progress. This book will be of great interest to a broad audience, particularly neuroscientists and AI researchers, investors in AI projects, and lay readers looking for an accessible introduction to the intersection of neuroscience and artificial intelligence.
Book Synopsis Epistemology and Emotions by : Georg Brun
Download or read book Epistemology and Emotions written by Georg Brun and published by Routledge. This book was released on 2016-04-29 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Undoubtedly, emotions sometimes thwart our epistemic endeavours. But do they also contribute to epistemic success? The thesis that emotions 'skew the epistemic landscape', as Peter Goldie puts it in this volume, has long been discussed in epistemology. Recently, however, philosophers have called for a systematic reassessment of the epistemic relevance of emotions. The resulting debate at the interface between epistemology, theory of emotions and cognitive science examines emotions in a wide range of functions. These include motivating inquiry, establishing relevance, as well as providing access to facts, beliefs and non-propositional aspects of knowledge. This volume is the first collection focusing on the claim that we cannot but account for emotions if we are to understand the processes and evaluations related to empirical knowledge. All essays are specifically written for this collection by leading researchers in this relatively new and developing field, bringing together work from backgrounds such as pragmatism and scepticism, cognitive theories of emotions and cognitive science, Cartesian epistemology and virtue epistemology.
