Scaling Deep Network

Scaling deep neural networks focuses on improving their performance and efficiency by increasing their size or complexity. Current research investigates scaling laws for various architectures, including multi-layer perceptrons (MLPs), convolutional neural networks (ConvNets), and graph neural networks (GNNs), employing techniques like masked autoencoders and novel optimization algorithms such as Mesh Adaptive Direct Search. These efforts aim to understand the relationship between model size, training data, and performance, ultimately leading to more powerful and efficient models for diverse applications. The findings inform the design of more effective training strategies and architectures, impacting both theoretical understanding and practical deployment of deep learning.