Adversarial networks are notoriously hard to train, and simple training methods often collapse. We present a simple modification to the standard training method that increases stability. The method is provably stable for a class of saddle-point problems, and improves performance of numerous GANs.
Neural net parameters can often be compressed down to just one single bit without a significant loss in network performance, yielding a huge reduction in memory footprint and computational workload. We develop a theory of quantized nets, and explain the performance of algorithms for weight quantization.
Classical machine learning methods, include stochastic gradient descent (aka backprop), work great on one machine, but don’t scale well to the cloud or cluster setting. We propose a variety of algorithmic frameworks for scaling machine learning across many workers.
