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) architecture to generate synthetic samples for variable length, multi-feature sequence datasets as existing GAN models cannot generate synthetic data and associated labels. In this model, we develop a GAN architecture with an additional autoencoder component, where recurrent neural networks (RNNs) are used for each component of the model in order to generate synthetic data to improve classification accuracy for a highly imbalanced medical device dataset. In addition to the medical device dataset, we also evaluate the GAN-AE performance on two additional datasets and demonstrate the application of GAN-AE to a sequence-to-sequence task where both synthetic sequence inputs and sequence outputs must be generated. To evaluate the quality of the synthetic data, we train encoder-decoder models both with and without the synthetic data and compare the classification model performance. We show that a model trained with GANAE generated synthetic data outperforms models trained with synthetic data generated both with standard oversampling techniques such as SMOTE and Autoencoders as well as with state of the art GAN-based models.

While many methods such as Locally Interpretable Model-agnostic Explanation (LIME), Integrated Gradients and Layerwise Relevance Propagation (LRP) have been developed to explain how recurrent neural networks make predictions, the explanations generated by each method often times vary dramatically. There is no consensus about which explainability method most accurately and robustly determine features important for model prediction. We consider a classification task on a sequence of events with different types and apply both gradient-based and attention-based explanation models to compute explanations on the event type level. We show that attention-based models return a higher similarity score between explanations for models initialized with different random seeds. However, there are still significant differences in explanations between model runs. We develop an optimization-based model to find a low-loss, high-accuracy path between two sets of trained weights to understand how model explanations morph between different local minima. We use this low-loss path to provide insight as to why explanations vary on two sentiment datasets.