Deep Learning Theory

Deep learning theory seeks to understand the mathematical principles underlying the remarkable success of deep neural networks. Current research focuses on explaining generalization ability, particularly in overparameterized models, investigating the role of inductive biases in various architectures (like MLPs, Transformers, and Graph Neural Networks), and analyzing training dynamics through lenses such as the neural tangent kernel. These theoretical advancements are crucial for improving the design, training, and interpretability of deep learning models, ultimately leading to more robust and reliable applications across diverse scientific and engineering domains.