Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely upto-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

Neural Networks And Learning Machines

1980 Preface * 1999 Preface * 1999 Acknowledgements * Introduction * 1 Circular Logic * 2 Phase Singularities (Screwy Results of Circular Logic) * 3 The Rules of the Ring * 4 Ring Populations * 5 Getting Off the Ring * 6 Attracting Cycles and Isochrons * 7 Measuring the Trajectories of a Circadian Clock * 8 Populations of Attractor Cycle Oscillators * 9 Excitable Kinetics and Excitable Media * 10 The Varieties of Phaseless Experience: In Which the Geometrical Orderliness of Rhythmic Organization Breaks Down in Diverse Ways * 11 The Firefly Machine 12 Energy Metabolism in Cells * 13 The Malonic Acid Reagent ('Sodium Geometrate') * 14 Electrical Rhythmicity and Excitability in Cell Membranes * 15 The Aggregation of Slime Mold Amoebae * 16 Numerical Organizing Centers * 17 Electrical Singular Filaments in the Heart Wall * 18 Pattern Formation in the Fungi * 19 Circadian Rhythms in General * 20 The Circadian Clocks of Insect Eclosion * 21 The Flower of Kalanchoe * 22 The Cell Mitotic Cycle * 23 The Female Cycle * References * Index of Names * Index of Subjects

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/596B270197FE27DE5FABB598B6210622/S0025557200150892a.pdf/div-class-title-span-class-italic-the-geometry-of-biological-time-span-by-a-t-winfree-pp-544-dm68-corrected-second-printing-1990-isbn-3-540-52528-9-springer-div.pdf

The geometry of biological time , by A. T. Winfree. Pp 544. DM68. Corrected Second Printing 1990. ISBN 3-540-52528-9 (Springer)

When a target speech signal is obscured by interfering speech, two distinct types of masking contribute to the resulting degradation in the intelligibility of the target talker: energetic masking caused by overlap in the time‐frequency distribution of energy in the two voices, and informational masking caused by the listener’s inability to correctly segregate the acoustic elements of the two voices into distinct streams. This study attempted to isolate the effects of energetic masking on multitalker speech perception with ideal time‐frequency binary masks that retained those spectro‐temporal regions of the acoustic mixture that were dominated by the target speech but eliminated those regions that were dominated by the interfering speech. This procedure removed the same phonetic information from the target speech that would ordinarily be lost due to energetic masking, but eliminated the possibility for the kinds of target‐masker confusions that are thought to produce informational masking. The results sugg...

A binary masking technique for isolating energetic masking in speech perception

In this work we describe methods for using the directionality of sound energy as a criterion to estimate single- and multichannel linear filters for suppression of diffuse noise and reverberation in a hearing aid application. We compare conservative strategies where direction of arrival is unknown, and more aggressive strategies where the proposed methods can be used to derive a fast acting post-filter for the output of a beamformer. We show that in situations where a target of interest is near to the listener while interfering sources are more distant, simple features that capture the directionality of sound energy can be used to attenuate significant undesired signal energy and can be more effective than a strategy based on noise-floor tracking.

/pdf/directionality-based-speech-enhancement-for-hearing-aids-2uuhwuxx0b.pdf

Directionality-based speech enhancement for hearing aids

Auditory segmentation is critical for complex auditory pattern processing. We present a neural network framework for auditory pattern segmentation. The network is a laterally coupled two-dimensional neural oscillators with a global inhibitor. One dimension represents time and another one represents frequency. We show that this architecture can in real time group auditory features into a segment by phase synchrony and segregate different segments by desynchronization. The network demonstrates the phenomenon that auditory stream segregation critically depends on the rate of presentation. The neuroplausibility of the model is discussed.

An oscillation model of auditory stream segregation

Prior models of speech have been used in robust automatic speech recognition to enhance noisy speech. Typically, a single prior model is trained by pooling the entire training data. In this paper we propose to train multiple prior models of speech instead of a single prior model. The prior models can be trained based on distinct characteristics of speech. In this study, they are trained based on voicing characteristics. The trained prior models are then used to reconstruct noisy speech. Significant improvements are obtained on the Aurora-4 robust speech recognition task when multiple priors are used; in conjunction with an uncertainty transform technique, multiple priors yield a 13.7% absolute improvement in the average word error rate over directly recognizing noisy speech.

/pdf/robust-speech-recognition-using-multiple-prior-models-for-4b3cjkgl12.pdf

Robust speech recognition using multiple prior models for speech reconstruction

An image segmentation method is proposed for texture analysis. The method is composed of two main parts. The first part determines a novel set of texture features based on Gaussian Markov random field (GMRF). Unlike other GMRF-based methods, our method is not limited by a fixed set of texture types. The second part is LEGION (Locally Excitatory Globally Inhibitory Oscillator Networks) which is a 2D array of neural oscillators. The coupling strengths between neighboring oscillators are calculated based on texture feature differences. When LEGION is simulated, the oscillators corresponding to the same texture tend to oscillate in synchrony, whereas different texture regions tend to attain different phases. Results demonstrating the success of our method on real texture images are provided.

DeLiang Wang

Papers

A binary masking technique for isolating energetic masking in speech perception

Directionality-based speech enhancement for hearing aids

An oscillation model of auditory stream segregation

Robust speech recognition using multiple prior models for speech reconstruction

Texture segmentation using Gaussian Markov random fields and LEGION