Scalable External Memories for Neural Networks
Short description: A study in scalable external memories for neural networks’s limitations on their capacity and computational complexity. This study examined 2 directions: proving theoretical limitations on single-pass full-capacity learning rules for linear threashold model as memory, and developing a more efficient vector-symbolic key-value memory architecture with linearithmic clean-up complexity.