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Full Description
Neural networks are typically considered a class of deep learning machines, a topic we discussed in detail in our Handbook of Statistics. In general usage, neural networks with fewer than four layers are often referred to as shallow networks, whereas architectures with four or more layers are commonly called. Deep learning models. A standard neural network consists of three main types of layers: the input layer, one or more hidden layers, and the output layer. These definitions are intended for broad understanding, and individual researchers or subfields may adopt slightly different conventions. The foundational ideas of neural networks have played a central role in the development of modern language models, machine‑learning‑based decision‑making systems, and many other advances in artificial intelligence.
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
