Author(s) Name:  Lan Zou

About the Book:

   Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning, giving an understanding of the concept of learning to learn. After giving a background on seven mainstream paradigms, such as machine learning, deep learning, and transfer learning, the book delves into nine important state-of-the-art mechanisms for meta-learning: memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm for meta-learning. It then demonstrates various meta-learning applications with nearly 200 state-of-the-art meta-learning algorithms in computer vision, natural language processing, meta-reinforcement learning, health care, finance and economy, construction materials, graphic neural networks, program synthesis, transportation, recommended systems, and climate science. Meta-Learning: Theory, Algorithms and Applications is the perfect companion to students and researchers who want to understand of the principles and state-of-the-art meta-learning algorithms, enabling the use of meta-learning for a range of applications.

Table of Contents

1. Meta-Learning Basics and Background
2. Model-Based Meta-Learning Approaches
3. Metric-Based Meta-Learning Approaches
4. Optimization-Based Meta-Learning Approaches
5. Meta-Learning for Computer Vision
6. Meta-Learning for Natural Language Processing
7. Meta-Reinforcement Learning
8. Meta-Learning for Health Care
9. Meta-Learning for Emerging Applications: Finance, Building Material, Graph Neural Networks, Program Synthesis, Transportation, Recommendation Systems and Climate Science

ISBN:  9780323899314

Publisher:  Academic Press

Year of Publication:  2022

Book Link:  Home Page Url