Name: Structured Representation Learning: From Homomorphisms and Disentanglement to Equivariance and Topography (Synthesis Lectures on Computer Vision) (en Inglés)
Price: 163.63 PEN
Availability: InStock
Author: Yue Song; Thomas Anderson Keller; Nicu Sebe; Max Welling
ISBN: 9783031881107

portada Structured Representation Learning: From Homomorphisms and Disentanglement to Equivariance and Topography (Synthesis Lectures on Computer Vision) (en Inglés)

Structured Representation Learning: From Homomorphisms and Disentanglement to Equivariance and Topography (Synthesis Lectures on Computer Vision) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Yue Song; Thomas Anderson Keller; Nicu Sebe; Max Welling

Idioma

Inglés

N° páginas

150

Encuadernación

Tapa Dura

ISBN13

9783031881107

Structured Representation Learning: From Homomorphisms and Disentanglement to Equivariance and Topography (Synthesis Lectures on Computer Vision) (en Inglés)

Yue Song; Thomas Anderson Keller; Nicu Sebe; Max Welling (Autor) · Springer · Tapa Dura

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Reseña del libro "Structured Representation Learning: From Homomorphisms and Disentanglement to Equivariance and Topography (Synthesis Lectures on Computer Vision) (en Inglés)"

This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms. In the field of machine learning, the idea of incorporating knowledge of data symmetries into artificial neural networks is known as equivariant deep learning and has led to the development of cutting edge architectures for image and physical data processing. The power of these models originates from data-specific structures ingrained in them through careful engineering. To-date however, the ability for practitioners to build such a structure into models is limited to situations where the data must exactly obey specific mathematical symmetries. The authors discuss naturally inspired inductive biases, specifically those which may provide types of efficiency and generalization benefits through what are known as homomorphic representations, a new general type of structured representation inspired from techniques in physics and neuroscience. A review of some of the first attempts at building models with learned homomorphic representations are introduced. The authors demonstrate that these inductive biases improve the ability of models to represent natural transformations and ultimately pave the way to the future of efficient and effective artificial neural networks.