Images are for illustrative purposes only - Read Full Disclaimer
Visual Cortex and Deep Networks: Learning Invariant Representations (Computational Neuroscience Series)
Book: Hardcover 23 - 09 - 2016Product ID: 4451674
Condition: New
Publisher : The MIT Press
Language : English
Hardcover : 136 Pages
ISBN-10 : 0262034727
Review Poggio and Anselmi present a scholarly and rigorous theoretical framework supported by experimental findings and computational simulations of how to build robust and invariant representations. Visual Cortex and Deep Networks features recent theoretical developments, which enable us to formalize the notion of how deep hierarchical structures can provide flexible image representations. Highly recommended.―Gabriel Kreiman, Associate Professor, Children's Hospital, Harvard Medical School Product Description A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex. About the Author Tomaso A. Poggio is Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at MIT, where he is also Director of the Center for Brains, Minds, and Machines and Codirector of the Center for Biological and Computational Learning. He is coeditor of Perceptual Learning (MIT Press). Fabio Anselmi is a Postdoctoral Fellow in the Istituto Italiano di Tecnologia Laboratory for Computational and Statistical Learning at MIT and part of the Center for Brains, Minds, and Machines.
Language : English
Hardcover : 136 Pages
ISBN-10 : 0262034727
Original Product Guaranteed - Imported from USA
Review Poggio and Anselmi present a scholarly and rigorous theoretical framework supported by experimental findings and computational simulations of how to build robust and invariant representations. Visual Cortex and Deep Networks features recent theoretical developments, which enable us to formalize the notion of how deep hierarchical structures can provide flexible image representations. Highly recommended.―Gabriel Kreiman, Associate Professor, Children's Hospital, Harvard Medical School Product Description A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex. About the Author Tomaso A. Poggio is Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at MIT, where he is also Director of the Center for Brains, Minds, and Machines and Codirector of the Center for Biological and Computational Learning. He is coeditor of Perceptual Learning (MIT Press). Fabio Anselmi is a Postdoctoral Fellow in the Istituto Italiano di Tecnologia Laboratory for Computational and Statistical Learning at MIT and part of the Center for Brains, Minds, and Machines.
Have a look at the full range of genuine products and brands in our Medicine and Neuroscience categories that you can safely buy online in United Arab Emirates (UAE) at discounted prices.
Get the lowest prices for products from the The MIT Press brand. The delivery of The MIT Press products is free and fast to your doorstep whether its your office, home or wherever you like!.
- local_shipping
FREE DELIVERY
Free shipping for orders over 250.00
- assignment_return
EASY RETURNS
Most items are eligible for return/exchange
- check
100% AUTHENTIC PRODUCTS
All products on Whizz.ae are 100% genuine
- lock
SAFE & SECURE SHOPPING
Your data is protected, encrypted and fully secure.
- live_help
WE ARE HERE TO HELP – CLICK HERE live_help
Got questions about this product or anything else?