With the latest advances in deep learning-based generative models, it has not taken long to
take advantage of their remarkable performance in the area of time series. Deep neural …

Data augmentation, the artificial creation of training data for machine learning by
transformations, is a widely studied research field across machine learning disciplines …

Learning is using past experiences to inform new behaviors and actions. Because all
experiences are unique, learning always requires some generalization. An effective way of …

Abstract State-of-charge (SOC) estimation of lithium-ion battery is one of the core functions
of battery management system. In order to improve the estimation accuracy of SOC, this …

In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

Deep learning performs remarkably well on many time series analysis tasks recently. The
superior performance of deep neural networks relies heavily on a large number of training …

Membership inference is one of the simplest privacy threats faced by machine learning
models that are trained on private sensitive data. In this attack, an adversary infers whether a …

We present EDA: easy data augmentation techniques for boosting performance on text
classification tasks. EDA consists of four simple but powerful operations: synonym …

Based on recent advances in natural language modeling and those in text generation
capabilities, we propose a novel data augmentation method for text classification tasks. We …

Regularization is a fundamental technique to prevent over-fitting and to improve
generalization performances by constraining a model's complexity. Current Deep Networks …