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)

Listening in a multitalker scenario, we typically attend to a single talker through auditory selective attention. Inspired by hu-man selective attention, we propose attentive training: a new training framework for talker-independent speaker extraction with an intrinsic selection mechanism. In the real world, multiple talkers very unlikely start speaking at the same time. Based on this observation, we train a deep neural network to create a representation for the first speaker and utilize it to extract or track that speaker from a multitalker noisy mixture. Experimental results demonstrate the superiority of attentive training over widely used permutation invariant training for talker-independent speaker extraction, especially in mismatched conditions in terms of the number of speakers, speaker interaction patterns, and the amount of speaker overlaps.

Attentive Training: A New Training Framework for Talker-independent Speaker Extraction

Speech enhancement aims to improve the listening quality and intelligibility of noisy speech in adverse environments. It proves to be challenging to perform speech enhancement in very low signal-to-noise ratio (SNR) conditions. Conventional speech enhancement utilizes air-conduction (AC) microphones, which are sensitive to background noise but capable of capturing full-band signals. On the other hand, bone-conduction (BC) sensors are unaffected by acoustic noise, but recorded speech has limited bandwidth. This study proposes an attention-based fusion method to combine the strengths of AC and BC signals and perform complex spectral mapping for speech enhancement. Experiments on the EMSB dataset demonstrate that the proposed approach effectively leverages the advantages of AC and BC sensors, and outperforms a recent time-domain baseline in all conditions. We also show that the sensor fusion method is superior to single-sensor counterparts, especially in low SNR conditions. As the amount of BC data is very limited, we additionally propose a semi-supervised technique to utilize both parallelly and unparallely recorded AC and BC speech signals. With additional AC speech from the AISHELL-1 dataset, we achieve similar performance to supervised learning with only 50% parallel data.

Fusing Bone-Conduction and Air-Conduction Sensors for Complex-Domain Speech Enhancement

The Kalman ﬁlter is widely used for addressing acoustic echo cancellation (AEC) problems due to their robustness to double-talk and fast convergence. However, the inability to model nonlinearity and the need to tune control parameters cast limitations on such adaptive ﬁltering algorithms. In this paper, we integrate the frequency domain Kalman ﬁlter (FDKF) and deep neural networks (DNNs) into a hybrid method, called KalmanNet, to leverage the advantages of deep learning and adaptive ﬁltering algorithms. Speciﬁcally, we employ a DNN to estimate nonlinearly distorted far-end signals, a transition factor, and the nonlinear transition function in the state equation of the FDKF algorithm. Experimental results show that the proposed KalmanNet improves the performance of FDKF signiﬁcantly and outperforms strong baseline methods.

KalmanNet: A Learnable Kalman Filter for Acoustic Echo Cancellation

PDF : Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part I (Lecture Notes In ... Computer Science And General Issues) Doc : Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part I (Lecture Notes In ... Computer Science And General Issues) ePub : Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part I (Lecture Notes In ... Computer Science And General Issues)

Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part II (Lecture Notes in Computer Science)

This paper presented our work on applying Recurrent Deep Stacking Networks (RDSNs) to Robust Automatic Speech Recognition (ASR) tasks. In the paper, we also proposed a more efficient yet comparable substitute to RDSN, Bi- Pass Stacking Network (BPSN). The main idea of these two models is to add phoneme-level information into acoustic models, transforming an acoustic model to the combination of an acoustic model and a phoneme-level N-gram model. Experiments showed that RDSN and BPsn can substantially improve the performances over conventional DNNs.

DeLiang Wang

Papers

Attentive Training: A New Training Framework for Talker-independent Speaker Extraction

Fusing Bone-Conduction and Air-Conduction Sensors for Complex-Domain Speech Enhancement

KalmanNet: A Learnable Kalman Filter for Acoustic Echo Cancellation

Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part II (Lecture Notes in Computer Science)

Recurrent Deep Stacking Networks for Speech Recognition.