Data augmentation has been widely used to improve generalizability of machine learning models. However, comparatively little work studies data for graphs. This is largely due the complex, non-Euclidean structure graphs, which limits possible manipulation operations. Augmentation operations commonly in vision and language have no analogs Our graph neural networks (GNNs) context improving semi-su...

Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance of using more data GAN training. Yet it is expensive to collect many domains such as medical applications. Data Augmentation (DA) has been applied these In this work, we first argue that classical DA approach could mislead generator learn distribution augmented data, which be different from original data. We ...

Piecewise regression represents a powerful tool to derive accurate yet modular models describing complex phenomena or physical systems. This paper presents an approach for learning PieceWise NonLinear (PWNL) functions in both supervised and semi-supervised setting. We further equip the proposed technique with method automatic generation of additional unsupervised data, which are leveraged impro...

In the last decade, there have been advances in machine learning performance various domains, including image classification, natural language processing, and speech recognition. The increase size of training data is essential for improvement these domains. two ways to larger sets are acquiring more original employing effective augmentation techniques. However, stock prediction studies, sizes d...

Data augmentation (DA) algorithms are widely used for Bayesian inference due to their simplicity. In massive data settings, however, DA prohibitively slow because they pass through the full in any iteration, imposing serious restrictions on usage despite advantages. Addressing this problem, we develop a framework extending that exploits asynchronous and distributed computing. The extended algor...

Abstract Natural Language Processing (NLP) is one of the most captivating applications Deep Learning. In this survey, we consider how Data Augmentation training strategy can aid in its development. We begin with major motifs summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, distinction between meaning form. follow these a concr...

Abstract A problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, are prone overfitting. Many methods have been proposed overcome this shortcoming CNNs. In cases where additional samples cannot easily be collected, a common approach generate more data points from existing using an augmentation technique. image clas...