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Author: Kshitij Dwivedi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Kshitij Dwivedi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Anh M. Nguyen
Publisher:
ISBN: 9780355324389
Category : Artificial intelligence
Languages : en
Pages : 226
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Book Description
Deep Learning, a type of Artificial Intelligence, is transforming many industries including transportation, health care and mobile computing. The main actors behind deep learning are deep neural networks (DNNs). These artificial brains have demonstrated impressive performance on many challenging tasks such as synthesizing and recognizing speech, driving cars, and even detecting cancer from medical scans. Given their excellent performance and widespread applications in everyday life, it is important to understand: (1) how DNNs function internally; (2) why they perform so well; and (3) when they fail. Answering these questions would allow end-users (e.g. medical doctors harnessing deep learning to assist them in diagnosis) to gain deeper insights into how these models behave, and therefore more confidence in utilizing the technology in important real-world applications. Artificial neural networks traditionally had been treated as black boxes—little was known about how they arrive at a decision when an input is present. Similarly, in neuroscience, understanding how biological brains work has also been a long-standing quest. Neuroscientists have discovered neurons in human brains that selectively fire in response to specific, abstract concepts such as Halle Berry or Bill Clinton, informing the discussion of whether learned neural codes are local or distributed. These neurons were identified by finding the preferred stimuli (here, images) that highly excite a specific neuron, which was accomplished by showing subjects many different images while recording a target neuron's activation. Inspired by such neuroscience techniques, my Ph.D. study produced a series of visualization methods that synthesize the preferred stimuli for each neuron in DNNs to shed more light into (1) the weaknesses of DNNs, which raise serious concerns about their widespread deployment in critical sectors of our economy and society; and (2) how DNNs function internally. Some of the notable findings are summarized as follows. First, DNNs are easily fooled in that it is possible to produce images that are visually unrecognizable to humans, but that state-of-the-art DNNs classify as familiar objects with near certainty confidence (i.e. labeling white-noise images as “school bus”). These images can be optimized to fool the DNN regardless of whether we treat the network as a white- or black-box (i.e. we have access to the network parameters or not). These results shed more light into the inner workings of DNNs and also question the security and reliability of deep learning applications. Second, our visualization methods reveal that DNNs can automatically learn a hierarchy of increasingly abstract features from the input space that are useful to solve a given task. In addition, we also found that neurons in DNNs are often multifaceted in that a single neuron fires for a variety of different input patterns (i.e. it is invariant to changes in the input). These observations align with the common wisdom previously established for both human visual cortex and DNNs. Lastly, many machine learning hobbyists and scientists have successfully applied our methods to visualize their own DNNs or even generate high-quality art images. We also turn the visualization frameworks into (1) an art generator algorithm, and (2) a state-of-the-art image generative model, making contributions to the fields of evolutionary computation and generative modeling, respectively.
Author: Kestutis Kveraga
Publisher: MIT Press
ISBN: 0262027852
Category : Science
Languages : en
Pages : 339
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Book Description
Cutting-edge research on the visual cognition of scenes, covering issues that include spatial vision, context, emotion, attention, memory, and neural mechanisms underlying scene representation. For many years, researchers have studied visual recognition with objects—single, clean, clear, and isolated objects, presented to subjects at the center of the screen. In our real environment, however, objects do not appear so neatly. Our visual world is a stimulating scenery mess; fragments, colors, occlusions, motions, eye movements, context, and distraction all affect perception. In this volume, pioneering researchers address the visual cognition of scenes from neuroimaging, psychology, modeling, electrophysiology, and computer vision perspectives. Building on past research—and accepting the challenge of applying what we have learned from the study of object recognition to the visual cognition of scenes—these leading scholars consider issues of spatial vision, context, rapid perception, emotion, attention, memory, and the neural mechanisms underlying scene representation. Taken together, their contributions offer a snapshot of our current knowledge of how we understand scenes and the visual world around us. Contributors Elissa M. Aminoff, Moshe Bar, Margaret Bradley, Daniel I. Brooks, Marvin M. Chun, Ritendra Datta, Russell A. Epstein, Michèle Fabre-Thorpe, Elena Fedorovskaya, Jack L. Gallant, Helene Intraub, Dhiraj Joshi, Kestutis Kveraga, Peter J. Lang, Jia Li Xin Lu, Jiebo Luo, Quang-Tuan Luong, George L. Malcolm, Shahin Nasr, Soojin Park, Mary C. Potter, Reza Rajimehr, Dean Sabatinelli, Philippe G. Schyns, David L. Sheinberg, Heida Maria Sigurdardottir, Dustin Stansbury, Simon Thorpe, Roger Tootell, James Z. Wang
Author: Charles Legéndy
Publisher: Springer
ISBN: 9780387888484
Category : Medical
Languages : en
Pages : 226
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Book Description
Dr. Charles Legéndy’s Circuits in the Brain: A Model of Shape Processing in the Primary Visual Cortex is published at a time marked by unprecedented advances in experimental brain research which are, however, not matched by similar advances in theoretical insight. For this reason, the timing is ideal for the appearance of Dr. Legéndy’s book, which undertakes to derive certain global features of the brain directly from the neurons. Circuits in the Brain, with its “relational firing” model of shape processing, includes a step-by-step development of a set of multi-neuronal networks for transmitting visual relations, using a strategy believed to be equally applicable to many aspects of brain function other than vision. The book contains a number of testable predictions at the neuronal level, some believed to be accessible to the techniques which have recently become available. With its novel approach and concrete references to anatomy and physiology, the monograph promises to open up entirely new avenues of brain research, and will be particularly useful to graduate students, academics, and researchers studying neuroscience and neurobiology. In addition, since Dr. Legéndy’s book succeeds in achieving a clean logical presentation without mathematics, and uses a bare minimum of technical terminology, it may also be enjoyed by non-scientists intrigued by the intellectual challenge of the elegant devices applied inside our brain. The book is uniquely self-contained; with more than 120 annotated illustrations it goes into full detail in describing all functional and theoretical concepts on which it builds.
Author: Stephen Grossberg
Publisher: Elsevier Science & Technology
ISBN:
Category : Computers
Languages : en
Pages : 300
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Book Description
An interdisciplinary book that surveys experimental and theoretical discoveries concerning how a brain sees and how insights about biological vision can be used to develop more effective algorithms for image processing in technology.
Author: Jamell A. Dozier
Publisher:
ISBN:
Category :
Languages : en
Pages : 70
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Book Description
Visual cognition has long been the subject of curiosity within the realm of deep learning. While much research has gone into the development of neural network models that can at times outperform humans, the underlying principles behind truly understanding visual concepts remain elusive. Utilizing a multitask learning paradigm, we first explore the capacity for networks to generalize to understand visual reasoning concepts. We introduce a simplified visual reasoning dataset to train several network architectures, including a recently proposed model built specifically for relational reasoning. We collect the best performing networks and view their behavior on a neuronal level: visualizing task selectivity through patterns of activations from each network layer. Finally, we adjust our focus to a simpler form of visual reasoning involving the extraction of single attributes from attribute compositions. Here, we are able to both visualize and quantify the neuron task selectivity that leads to generalization.
Author: Eshed Margalit
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Humans have the remarkable capacity to recognize visual objects despite challenging variations in their pose, illumination, and context. This ability depends on the ventral visual stream, a series of cortical areas that progressively transforms the signal from the retina into representations of object category, location, color, texture, and size. Our understanding of the function and development of the ventral visual stream is anchored in the tight coupling between structure and function in the constituent cortical areas: in each area, neurons are arranged in the cortical sheet according to the visual features they respond most strongly to. In the earliest stage of the ventral visual stream neighboring neurons preferentially respond to edges of similar orientations and colors, whereas neurons toward the end of the ventral stream cluster together according to their preferred object category, e.g., faces, limbs, and places. Understanding the development and purpose of this functional organization requires the construction of detailed models whose predictions can be evaluated against neural measurements. In this dissertation, I present topographic deep convolutional neural networks (topographic DCNNs) as unifying models of neural structure and function throughout the ventral visual stream. Topographic DCNNs implement the simple hypothesis that functional organization in the visual cortex can be reproduced by optimizing the parameters of a neural network to perform a challenging visual task while keeping local populations of neurons correlated with one another. I find that topographic DCNNs are able to reproduce functional organization in both early and later stages of the ventral visual stream, that this brain-model correspondence is strongest for more biologically-plausible learning algorithms, and that topographic DCNNs can be used to predict how changes to visual inputs during development will affect cortical map formation. The success of topographic DCNNs in the prediction of the functional organization of the primate ventral visual stream implies the existence of simple unifying principles for the development of those regions, and serves as a foundation from which increasingly accurate models of visual processing can be constructed.
Author: David Ingle
Publisher: MIT Press (MA)
ISBN:
Category : Psychology
Languages : en
Pages : 870
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Book Description
"Analysis of Visual Behavior" encompasses both theoretical and experimental research. It deals with the visual mechanisms of diverse vertebrate species from salamanders and toads to primates and humans and presents a stimulating interaction of the disciplines of anatomy, physiology, and behavioral science. Throughout, visual mechanisms are investigated from the point of view of the brain functioning at the organismic level, as opposed to the now more prevalent focus on the molecular and cellular levels. This approach allows researchers to deal with the patterns of visually guided behavior of animals in real-life situations.The twenty-six contributions in the book are divided among three sections: "Indentification and Localization Processes in Nonmammalian Vertebrates," introduced by David J. Ingle; "Visual Guidance of Motor Patterns: The Role of Visual Cortex and the Superior Colliculus," introduced by Melvyn A. Goodale; and "Recognition and Transfer Processes," introduced by Richard J. W. Mansfield.The editors are all university researchers in psychology: David J. Ingle at Brandeis, Melvyn A. Goodale at the University of Western Ontario, and Richard J. W. Mansfield at Harvard.
Author: Wojciech Samek
Publisher: Springer Nature
ISBN: 3030289540
Category : Computers
Languages : en
Pages : 435
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Book Description
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Author: Evian Gordon
Publisher: Oxford University Press
ISBN: 0195393805
Category : Language Arts & Disciplines
Languages : en
Pages : 346
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Book Description
This book takes an in depth and hard look at the current status and future direction of treatment predictive markers in Personalized Medicine for the brain from the perspectives of the researchers on the cutting edge and those involved in healthcare implementation. The contents provide a comprehensive text suitable as both a pithy introduction to and a clear summary of the "science to solutions" continuum in this developing field of Personalized Medicine and Integrative Neuroscience. The science includes both measures of genes using whole genome approaches and SNIPS as well as BRAINmarkers of direct brain function such as brain imaging, biophysical changes and objective cognitive and behavioral measurements. Personalized Medicine for Brain Disorders will soon be a reality using the comprehensive quantitative and standardized approaches to genomics, BRAINmarkers and cognitive function. Each chapter provides a review of recent relevant literature; show the solutions achieved through integrative neuroscience and applications in patient care thus providing a practical guide to the reader. The timeliness of this book's content is propitious providing bottom line information to educate practicing clinicians, health care workers and researchers, and also a pathway for undergraduate and graduates interested in further their understanding of and involvement in tailored personal solutions.
