39 citations · 154 across the 26 of their papers we have counts for
5 papers · 1 filter
Generative Adversarial Nets for Multiple Text Corpora
Baiyang Wang, Diego Klabjan
Generative adversarial nets (GANs) have been successfully applied to the artificial generation of image data. In terms of text data, much has been done on the artificial generation…
OSTSC: Over Sampling for Time Series Classification in R
Matthew Dixon, Diego Klabjan, Lan Wei
The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the oversampling…
Improving the Expected Improvement Algorithm
Chao Qin, Diego Klabjan, Daniel Russo
The expected improvement (EI) algorithm is a popular strategy for information collection in optimization under uncertainty. The algorithm is widely known to be too greedy, but neve…
Activation Ensembles for Deep Neural Networks
Mark Harmon, Diego Klabjan
Many activation functions have been proposed in the past, but selecting an adequate one requires trial and error. We propose a new methodology of designing activation functions wit…
Semi-supervised Learning for Discrete Choice Models
Jie Yang, Sergey Shebalov, Diego Klabjan
We introduce a semi-supervised discrete choice model to calibrate discrete choice models when relatively few requests have both choice sets and stated preferences but the majority…