Optimizing Neural Networks: The Lottery Ticket Analogy

This chapter explores the optimization of neural network architecture through the lens of the lottery ticket hypothesis, highlighting the importance of feature selection and over-parameterization. It discusses strategies for compressing networks, such as tensor factorization and channel pruning, while maintaining performance for applications like medical image analysis. The chapter also examines the complexities and iterative processes involved in determining optimal network sizes and pruning ratios to enhance computational efficiency.