Artificial intelligence research has seen enormous progress over the past few decades, but it
predominantly relies on fixed datasets and stationary environments. Continual learning is an …

Android applications are develo** rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

In this paper, we propose a novel edge-labeling graph neural network (EGNN), which
adapts a deep neural network on the edge-labeling graph, for few-shot learning. The …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

In this paper, we propose a novel meta learning approach for automatic channel pruning of
very deep neural networks. We first train a PruningNet, a kind of meta network, which is able …

Recent research on super-resolution has achieved greatsuccess due to the development of
deep convolutional neu-ral networks (DCNNs). However, super-resolution of arbi-trary scale …

Abstract Most applications of Artificial Intelligence (AI) are designed for a confined and
specific task. However, there are many scenarios that call for a more general AI, capable of …

Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate
understanding of the sequence (-structure)-function paradigm for DNAs, RNAs and proteins …

Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …