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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Preface - p. xv 8. Chapter 1: Artificial neural network procedures for the nonlinear dynamical plankton system - p. 1 9. Chapter 2: Testing trained models as null hypotheses to assess their absolute performance, with applications to testing neural networks as null hypotheses - p. 17 10. Chapter 3: Deep learning for stochastic partial differential equations - p. 31 11. Chapter 4: Test-time adaptation with neural networks: approaches and advances in image classification - p. 57 12. Chapter 5: Neural network-based control in rehabilitative and collaborative robotics - p. 93 13. Chapter 6: Random matrix perspective in deep learning: spectral analysis, training dynamics, and generalization - p. 121 14. Chapter 7: Neural networks from statistical perspective - p. 171 15. Chapter 8: Approximation theory for neural networks: old and new - p. 199 16. Chapter 9: Randomly weighted neural networks in statistical modeling - p. 225 17. Chapter 10: Recent advances in weakly supervised learning - p. 289 18. Chapter 11: Neural networks for official statistics: modeling high-dimensional structure in complex surveys and administrative records - p. 321 19. Index - p. 349
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Preface - p. xv 8. Chapter 1: Artificial neural network procedures for the nonlinear dynamical plankton system - p. 1 9. Chapter 2: Testing trained models as null hypotheses to assess their absolute performance, with applications to testing neural networks as null hypotheses - p. 17 10. Chapter 3: Deep learning for stochastic partial differential equations - p. 31 11. Chapter 4: Test-time adaptation with neural networks: approaches and advances in image classification - p. 57 12. Chapter 5: Neural network-based control in rehabilitative and collaborative robotics - p. 93 13. Chapter 6: Random matrix perspective in deep learning: spectral analysis, training dynamics, and generalization - p. 121 14. Chapter 7: Neural networks from statistical perspective - p. 171 15. Chapter 8: Approximation theory for neural networks: old and new - p. 199 16. Chapter 9: Randomly weighted neural networks in statistical modeling - p. 225 17. Chapter 10: Recent advances in weakly supervised learning - p. 289 18. Chapter 11: Neural networks for official statistics: modeling high-dimensional structure in complex surveys and administrative records - p. 321 19. Index - p. 349
