Modern Neural Network Architecture

Modern neural network architecture research focuses on improving efficiency, interpretability, and continual learning capabilities of deep learning models. Current efforts involve developing novel optimization techniques like Kronecker-Factored Approximate Curvature (K-FAC) to accelerate training, exploring lower-precision arithmetic (e.g., FP8) to reduce computational costs, and designing architectures that mitigate catastrophic forgetting during continual learning. These advancements are crucial for deploying large-scale neural networks in resource-constrained environments and for building more trustworthy and understandable AI systems across diverse applications.