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)

Permutation ambiguity is a crucial issue for deep learning based talker-independent speaker separation. Deep clustering and permutation invariant training (PIT) have been widely used to address the permutation ambiguity problem in monaural scenarios. Although both approaches have been extended to multi-microphone scenarios, we believe that the permutation ambiguity problem can be naturally avoided by leveraging the spatial relations of multiple speakers. In this article, we present location-based training (LBT), a new approach to achieve talker independency in multi-channel speaker separation. Unlike PIT that examines all possible permutations, LBT assigns speakers according to their positions in physical space. With a linear training complexity to the number of concurrent speakers, LBT is computationally much more efficient than PIT with a factorial complexity, particularly when a large number of overlapping speakers needs to be separated. Specifically, we propose two training criteria: azimuth-based and distance-based training, using speaker azimuths and distances relative to a microphone array, respectively. Evaluation results show that LBT significantly outperforms PIT on two-speaker and three-speaker mixtures with different array geometries and in various acoustic conditions. In addition, we propose a joint training strategy to integrate azimuth-based and distance-based training, which further improves separation performance.

Multi-Channel Talker-Independent Speaker Separation Through Location-Based Training

The central theme is a quest for models of learning using the neural network approach. There are two parts to the dissertation: a formal neural network model of temporal order learning and a biological neural model for stimulus-specific habituation in toads. In the first part, learning mechanisms are explored at the abstract level, i.e., we design neural networks to learn, recognize, and reproduce complex temporal sequences. Short-term memory is modeled by a network of neural units with mutual inhibition. Sequences are acquired for long-term memory with a new rule, called the attentional learning rule, that combines Hebbian rule and a normalization rule with sequential system activation. Acquired sequences can be recognized without being affected by speeds in presentation and certain distortions in symbol forms. Sequence reproduction is achieved with two reciprocally connected layers, and reproduction of complex sequences can maintain the temporal course of learned sequences. Different layers of the model can be constructed in a feedforward manner to recognize hierarchically organized temporal structures, in a way similar to human information chunking.
In the second part, learning mechanisms are studied at the neurobiological level with a specific animal. A computational model is first presented for visual pattern discrimination in toads. The anterior thalamus (AT) model integrates visual inputs from the retina and the tectum, and produces orderly average firing activities in response to the stimuli in a dishabituation hierarchy. The output from the AT model is fed to the model of the medial pallium (MP), where neuronal responses to the stimuli are further processed and stored. A model of synaptic plasticity is proposed for MP as an interaction of two dynamic processes which simulates acquisition and both short- and long-term forgetting. Large-scale computer simulations demonstrate that the model of the interacting brain structures can reproduce experimental data remarkably well. The model of AT and MP structures yields a range of experimental predictions concerning the properties of learning and pattern discrimination. Initial model testing experiments have validated certain predictions and lead to new findings of behavioral phenomena. (Copies available exclusively from Micrographics Department, Doheny Library, USC, Los Angeles, CA 90089-0182.)

Neural networks for temporal order learning and stimulus-specific habituation

We describe and evaluate a model of motion perception based on the integration of information from two parallel pathways: a motion pathway and a luminance pathway. The motion pathway has two stages. The first stage measures and pools local motion across the input animation sequence and assigns reliability indices to these pooled measurements. The second stage groups locations on the basis of these measurements. In the luminance pathway, the input scene is segmented into regions on the basis of similarities in luminance. In a subsequent integration stage, motion and luminance segments are combined to obtain the final estimates of object motion. The neural network architecture we employ is based on LEGION (locally excitatory globally inhibitory oscillator networks), a scheme for feature binding and region labeling based on oscillatory correlation. Many aspects of the model are implemented at the neural network level, whereas others are implemented at a more abstract level. We apply this model to the computation of moving, uniformly illuminated, two-dimensional surfaces that are either opaque or transparent. Model performance replicates a number of distinctive features of human motion perception.

/pdf/an-oscillatory-correlation-model-of-visual-motion-analysis-1p1qfzqtas.pdf

An oscillatory correlation model of visual motion analysis

Fostering deep learning and beyond

Cochannel speech separation is the task of separating two speech signals from a single mixture. The task becomes even more challenging if the speech mixture is further corrupted by background noise. In this study, we focus on a genderdependent scenario, where target speech is from a male speaker and interfering speech from a female speaker. We propose a two-stage separation strategy to address this problem in a noise-independent way. In the proposed system, denoising and cochannel separation are performed successively by two modules, which are based on a newly-introduced convolutional neural network for speech separation. The evaluation results demonstrate that the proposed system substantially outperforms one-stage baselines in terms of objective intelligibility and perceptual quality.

DeLiang Wang

Papers

Multi-Channel Talker-Independent Speaker Separation Through Location-Based Training

Neural networks for temporal order learning and stimulus-specific habituation

An oscillatory correlation model of visual motion analysis

Fostering deep learning and beyond

A Two-Stage Approach to Noisy Cochannel Speech Separation with Gated Residual Networks.