Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis. A central goal of this book is to show that these analytical tools can be generalized from their usual setting in (infinite-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete representation of the space must be adaptively acquired; basis functions are not pre-defined, but rather must be constructed. Algorithms for efficiently computing and representing bases require dealing with the curse of dimensionality. However, the benefits can outweigh the costs, since the extracted basis functions outperform parametric bases as they often reflect the irregular shape of a particular state space. Case studies from computer graphics, information retrieval, machine learning, and state space planning are used to illustrate the benefits of the proposed framework, and the challenges that remain to be addressed. Representation discovery is an actively developing field, and the author hopes this book will encourage other researchers to explore this exciting area of research. Table of Contents: Overview / Vector Spaces / Fourier Bases on Graphs / Multiscale Bases on Graphs / Scaling to Large Spaces / Case Study: State-Space Planning / Case Study: Computer Graphics / Case Study: Natural Language / Future Directions
Provides a comprehensive survey of techniques to automatically construct basis functions or features for value function approximation in Markov decision processes and reinforcement learning.
Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis. A central goal of this book is to show that these analytical tools can be generalized from their usual setting in (infinite-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete representation of the space must be adaptively acquired; basis functions are not pre-defined, but rather must be constructed. Algorithms for efficiently computing and representing bases require dealing with the curse of dimensionality. However, the benefits can outweigh the costs, since the extracted basis functions outperform parametric bases as they often reflect the irregular shape of a particular state space. Case studies from computer graphics, information retrieval, machine learning, and state space planning are used to illustrate the benefits of the proposed framework, and the challenges that remain to be addressed. Representation discovery is an actively developing field, and the author hopes this book will encourage other researchers to explore this exciting area of research. Table of Contents: Overview / Vector Spaces / Fourier Bases on Graphs / Multiscale Bases on Graphs / Scaling to Large Spaces / Case Study: State-Space Planning / Case Study: Computer Graphics / Case Study: Natural Language / Future Directions
Provides a comprehensive survey of techniques to automatically construct basis functions or features for value function approximation in Markov decision processes and reinforcement learning.
Mycology is a frontier area of research in life sciences. Fungi represent one of the three major evolutionary segments along with plants and animals. Fungal multidimensional features with basic and applied value projected their potential beyond routine systematics, diversity and environmental studies. In view of tremendous developments in the field of Mycology, the present treatise emphasizes various aspects of contemporary issues in mycology. It comprises 22 chapters with emphasis on the fungal ecology, diversity and metabolites. The topics treated include aquatic ecology, diversity and phylogeny, mutualism and interactions, potential metabolites, pathology and toxins, fungal infections and prevention, cell permeabilization and advances in monocarboxylate transporters in yeasts with an emphasis on cancer therapy. This volume is of special interest to mycologists as a valuable source of information on the frontier areas of mycology dealing with diversity, ecological amplitudes, methods of assessment, novel metabolites and bioprospecting avenues
The book will be a landmark in itself because it will be the first to cover behind the scenes of every loved ad, right from the Doordarshan days to today's YouTube; right from 'Chal meri luna' to 'Airtel smartphone ads'. It will cover interviews of creative heads and directors of all generations, right from vintage to new age. Author has handpicked each ad based on their popularity among viewers and met its creators and talked to them about the entire process. He had left out the marketing jargons and advertising sham, and just weaved stories using wonderful stories. The book will feature legendary ad-creators like Alyque Padamsee, Piyush Pandey, Prahlad Kakkar, R Balki, Prasoon Joshi, Prasoon Pandey, Agnello Dias, KS Chakravarty, Prakash Varma, Nitesh Tiwari, Preeti Nair, Ram Madhvani, Kailash Surendranath, Amit Sharma, Ashish Khajanji, Parshuraman, AG Krishnamurthy, Shantanu Sheorey and many more. One unique aspect about this book is the coming together of virtually the entire ad industry.
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