27 citations · 34 across the 6 of their papers we have counts for
3 papers · 1 filter
Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling
Daniel Stoller, Mi Tian, Sebastian Ewert +1
Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling…
Adaptive Loss Scaling for Mixed Precision Training
Ruizhe Zhao, Brian Vogel, Tanvir Ahmed
Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support…
Chainer: A Deep Learning Framework for Accelerating the Research Cycle
Seiya Tokui, Ryosuke Okuta, Takuya Akiba +7
Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intend…