Probabilistic Binary Neural Networks

Jorn W. T. Peters; Max Welling

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arXiv · 1809.03368

Probabilistic Binary Neural Networks

Jorn W. T. Peters·Max Welling

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Abstract

Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a probabilistic training method for Neural Network with both binary weights and activations, called BLRNet. By embracing stochasticity during training, we circumvent the need to approximate the gradient of non-differentiable functions such as sign(), while still obtaining a fully Binary Neural Network at test time. Moreover, it allows for anytime ensemble predictions for improved performance and uncertainty estimates by sampling from the weight distribution. Since all operations in a layer of the BLRNet operate on random variables, we introduce stochastic versions of Batch Normalization and max pooling, which transfer well to a deterministic network at test time. We evaluate the BLRNet on multiple standardized benchmarks.

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Jorn W. T. Peters, Max Welling. 2018-09-10. Probabilistic Binary Neural Networks. https://arxiv.org/abs/1809.03368

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