3 citations · 10 across the 27 of their papers we have counts for
4 papers · 1 filter
Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network
Karl Pierce, Yuehaw Khoo, Haizhao Yang
In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN). This optimization procedure introduces conte…
Tensorizing flows: a tool for variational inference
Yuehaw Khoo, Michael Lindsey, Hongli Zhao
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribut…
High-dimensional density estimation with tensorizing flow
Yinuo Ren, Hongli Zhao, Yuehaw Khoo +1
We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data. The method is based on tensor-train and flow-based gene…
Drop-Activation: Implicit Parameter Reduction and Harmonic Regularization
Senwei Liang, Yuehaw Khoo, Haizhao Yang
Overfitting frequently occurs in deep learning. In this paper, we propose a novel regularization method called Drop-Activation to reduce overfitting and improve generalization. The…