12 citations · 14 across the 3 of their papers we have counts for
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cs.LG2019★ 2 cited
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification
Stephanie Ger, Yegna Subramanian Jambunath, Diego Klabjan
Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) archit…
cs.LG2018
Layer Flexible Adaptive Computational Time
Lida Zhang, Abdolghani Ebrahimi, Diego Klabjan
Deep recurrent neural networks perform well on sequence data and are the model of choice. However, it is a daunting task to decide the structure of the networks, i.e. the number of…