Although deep learning has achieved satisfactory performance in computer vision, a large
volume of images is required. However, collecting images is often expensive and …

This article surveys and organizes research works in a new paradigm in natural language
processing, which we dub “prompt-based learning.” Unlike traditional supervised learning …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

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

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

Approaches based on deep neural networks have achieved striking performance when
testing data and training data share similar distribution, but can significantly fail otherwise …

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 …

Recent work on automated augmentation strategies has led to state-of-the-art results in
image classification and object detection. An obstacle to a large-scale adoption of these …

In many machine learning problems, large-scale datasets have become the de-facto
standard to train state-of-the-art deep networks at the price of heavy computation load. In this …

Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …