Block Local Learning

Block local learning focuses on improving the efficiency and scalability of training large neural networks by dividing them into smaller, independently trainable blocks. Current research explores various approaches, including leveraging probabilistic latent representations to enable parallel processing and employing techniques like temporally-truncated backpropagation through time to reduce computational costs. This strategy offers significant advantages for handling massive datasets and complex architectures, particularly in applications like computational fluid dynamics and image processing where data reduction and efficient training are crucial.