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Will Williams

2 accepted papers

2020

Hierarchical Quantized Autoencoders

NeurIPS 2020poster

Despite progress in training neural networks for lossy image compression, current approaches fail to maintain both perceptual quality and abstract features at very low bitrates. Encouraged by recent success in learning discrete representations with Vector Quantized Variational Autoencoders (VQ-VAEs)…

2015

Scaling recurrent neural network language models

ICASSP 2015accepted

This paper investigates the scaling properties of Recurrent Neural Network Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and address the questions of how RNNLMs scale with respect to model size, training-set size, computational costs and memory. Our analysis shows that de…

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