%0 Journal Article
%T On-the-Fly Learning in a Perpetual Learning Machine
%A Andrew J. R. Simpson
%J Computer Science
%D 2015
%I arXiv
%X Despite the promise of brain-inspired machine learning, deep neural networks (DNN) have frustratingly failed to bridge the deceptively large gap between learning and memory. Here, we introduce a Perpetual Learning Machine; a new type of DNN that is capable of brain-like dynamic 'on the fly' learning because it exists in a self-supervised state of Perpetual Stochastic Gradient Descent. Thus, we provide the means to unify learning and memory within a machine learning framework. We also explore the elegant duality of abstraction and synthesis: the Yin and Yang of deep learning.
%U http://arxiv.org/abs/1509.00913v3