The High-Dimensional Geometry of Binary Neural Networks

ICLR 2018 Alexander G. AndersonCory P. Berg

Recent research has shown that one can train a neural network with binary weights and activations at train time by augmenting the weights with a high-precision continuous latent variable that accumulates small changes from stochastic gradient descent. However, there is a dearth of theoretical analysis to explain why we can effectively capture the features in our data with binary weights and activations... (read more)

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