27 citations · 34 across the 6 of their papers we have counts for
6 papers
An NMF-Based Building Block for Interpretable Neural Networks With Continual Learning
Brian K. Vogel
Existing learning methods often struggle to balance interpretability and predictive performance. While models like nearest neighbors and non-negative matrix factorization (NMF) off…
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…
Parameter Reference Loss for Unsupervised Domain Adaptation
Jiren Jin, Richard G. Calland, Takeru Miyato +2
The success of deep learning in computer vision is mainly attributed to an abundance of data. However, collecting large-scale data is not always possible, especially for the superv…
Positive factor networks: A graphical framework for modeling non-negative sequential data
Brian K. Vogel
We present a novel graphical framework for modeling non-negative sequential data with hierarchical structure. Our model corresponds to a network of coupled non-negative matrix fact…