Machine learning methods, especially deep models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, models are more demanding for training data. Data augmentation techniques widely used to generate new instances based on modifications existing data or relying external knowledge bases address annotated scarcity, which hinders...

Abstract With the continuous development of human life and society, medical field is constantly improving. However, modern medicine still faces many limitations, including challenging previously unsolvable problems. In these cases, artificial intelligence (AI) can provide solutions. The research application generative adversarial networks (GAN) are a clear example. While most researchers focus ...

Data augmentation is a widely used regularization technique for improving the performance of convolutional neural networks (CNNs) in image classification tasks. To improve effectiveness data augmentation, it important to find label-preserving transformations that fit domain knowledge given dataset. In several real-world datasets, appropriate policies differ between classes, owing their differen...

Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties. Solving the FWI problem be challenging due to its ill-posedness and high cost. In this work, we develop new hybrid approach solve combines physics-based models with data-driven methodologies. particular, data augmentation strategy not onl...