39 citations · 89 across the 8 of their papers we have counts for
16 papers
Data Extraction from Charts via Single Deep Neural Network
Xiaoyi Liu, Diego Klabjan, Patrick NBless
Automatic data extraction from charts is challenging for two reasons: there exist many relations among objects in a chart, which is not a common consideration in general computer v…
Dynamic Cell Structure via Recursive-Recurrent Neural Networks
Xin Qian, Matthew Kennedy, Diego Klabjan
In a recurrent setting, conventional approaches to neural architecture search find and fix a general model for all data samples and time steps. We propose a novel algorithm that ca…
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…