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Neural Networks

  • 1st Edition, Volume 55 - September 1, 2026
  • Latest edition
  • Editors: Arni S.R. Srinivasa Rao, Arni S.R. Srinivasa Rao
  • Language: English

Neural Networks, Volume 55 delves into the world of deep learning machines, defining neural networks and covering their central role in the development of modern language models… Read more

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Description

Neural Networks, Volume 55 delves into the world of deep learning machines, defining neural networks and covering their central role in the development of modern language models, machine‑learning‑based decision‑making systems, and many other advances in artificial intelligence. Chapters in this new release include Neural networks with random weights, Bayesian Neural Networks for Official Statistics: Modeling High-Dimensional Structure in Complex Surveys and Administrative Records, Weakly supervised learning for neural networks, How to test a neural network as a null hypothesis, Test-Time Adaptation with Neural Networks: Approaches and Advances in Image Classification, and much more.

Additional sections cover Semantics and Verification of Neural Network Components in Robotic Control Software, Artificial Neural Network Procedures for the Nonlinear Dynamical Plankton System, Neural Networks from Statistical Perspective, Neural Network applications in Assistive and Collaborative Robotics, Neural Network applications in Assistive and Collaborative Robotics, and Neural Networks using SPDEs.

Key features

  • Covers the latest developments in neural networks
  • Presents easy to understand concepts
  • Written by experts in the field of neural networks

Readership

Every effort will be made to get chapters written in a detailed and training fashion rather than typical journal-type articles. We hope to make the volume remain in the academic circles for years and to be used by students and advanced researchers.

Table of contents

Preface

1. Neural networks with random weights
Cira Perna and Michele La Rocca

2. Bayesian Neural Networks for Official Statistics: Modeling High-Dimensional Structure in Complex Surveys and Administrative Records
Scott H. Holan

3. weakly supervised learning for neural networks
Wei Wang, Gang Niu and Masashi Sugiyama

4. How to test a neural network as a null hypothesis
David Bickel

5. TBD
Soumendu Sundar Mukherjee

6. Test-Time Adaptation with Neural Networks: Approaches and Advances in Image Classification
Sravan Danda

7. Semantics and Verification of Neural Network Components in Robotic Control Software

8. Artificial Neural Network Procedures for the Nonlinear Dynamical Plankton System
Adnène Arbi Sr. and Walid Ben Ameur

9. Neural Networks from Statistical Perspective
Qi on Patent - Qi Meng

10. Neural Network applications in Assistive and Collaborative Robotics
Bingguang Chen

11. Neural Network applications in Assistive and Collaborative Robotics
Antonella Ferrara, NIKOLAS SACCHI, Gian Paolo Incremona, Edoardo Vacchini and Chiara Alessi

12. Neural Networks using SPDEs
Hua Li

Product details

  • Edition: 1
  • Latest edition
  • Volume: 55
  • Published: September 1, 2026
  • Language: English

About the editors

AS

Arni S.R. Srinivasa Rao

Arni S.R. Srinivasa Rao works in pure mathematics, applied mathematics, probability, artificial

intelligence and applications in medicine. He developed the concept of “Exact Deep Learning Machines”, which can provide designs for accurate predictions without any uncertainty. He had edited these handbooks jointly with renowned statistician Dr. C. R. Rao. He is a Professor at the Medical College of Georgia, Augusta University, U.S.A., and the Director of the Laboratory for Theory and Mathematical Modeling housed within the Division of Infectious Diseases, Medical College of Georgia, Augusta, U.S.A. Previously, Dr. Rao conducted research and/or taught at the Mathematical Institute, University of Oxford (2003, 2005-07), Indian Statistical Institute (1998-2002, 2006-2012), Indian Institute of Science (2002-04), University of Guelph (2004-06). Until 2012, Dr. Rao held a permanent faculty position at the Indian Statistical Institute. He has won the Heiwa-Nakajima Award (Japan) and Fast Track Young Scientists Fellowship in Mathematical Sciences (DST, New Delhi). Dr. Rao also proved a major theorem in stationary population models, such as, Rao’s Partition Theorem inPopulations, Rao-Carey Theorem in stationary populations, and developed mathematical modeling-based policies for the spread of diseases like HIV, H5N1, COVID-19, etc. He developed a new set of network models for understanding avian pathogen biology on grid graphs (these were called chicken walk models), AI Models for COVID-19, and received wide coverage in the science media. Dr. Rao is an elected Fellow of ISMMACS (Indian Society for Mathematical Modeling and Computer Simulation), and ISPS (Indian Society for Probability and Statistics). He developed concepts such as “Multilevel Contours within a bundle of Complex Number Planes”.

Affiliations and expertise
Medical College of Georgia, Augusta, U.S.A.

AS

Arni S.R. Srinivasa Rao

Arni S.R. Srinivasa Rao works in pure mathematics, applied mathematics, probability, artificial

intelligence and applications in medicine. He developed the concept of “Exact Deep Learning Machines”, which can provide designs for accurate predictions without any uncertainty. He had edited these handbooks jointly with renowned statistician Dr. C. R. Rao. He is a Professor at the Medical College of Georgia, Augusta University, U.S.A., and the Director of the Laboratory for Theory and Mathematical Modeling housed within the Division of Infectious Diseases, Medical College of Georgia, Augusta, U.S.A. Previously, Dr. Rao conducted research and/or taught at the Mathematical Institute, University of Oxford (2003, 2005-07), Indian Statistical Institute (1998-2002, 2006-2012), Indian Institute of Science (2002-04), University of Guelph (2004-06). Until 2012, Dr. Rao held a permanent faculty position at the Indian Statistical Institute. He has won the Heiwa-Nakajima Award (Japan) and Fast Track Young Scientists Fellowship in Mathematical Sciences (DST, New Delhi). Dr. Rao also proved a major theorem in stationary population models, such as, Rao’s Partition Theorem inPopulations, Rao-Carey Theorem in stationary populations, and developed mathematical modeling-based policies for the spread of diseases like HIV, H5N1, COVID-19, etc. He developed a new set of network models for understanding avian pathogen biology on grid graphs (these were called chicken walk models), AI Models for COVID-19, and received wide coverage in the science media. Dr. Rao is an elected Fellow of ISMMACS (Indian Society for Mathematical Modeling and Computer Simulation), and ISPS (Indian Society for Probability and Statistics). He developed concepts such as “Multilevel Contours within a bundle of Complex Number Planes”.

Affiliations and expertise
Medical College of Georgia, Augusta, U.S.A.