Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and
related applications, which aims to complete the structure of knowledge graph by predicting …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

Learning information from a single or a few samples is called few-shot learning. This
learning method will solve deep learning's dependence on a large sample. Deep learning …

Deep neural networks (DNNs) have had extraordinary successes in classifying
photographic images of objects and are often described as the best models of biological …

Recent research in artificial intelligence and machine learning has largely emphasized
general-purpose learning and ever-larger training sets and more and more compute. In …

Although deep learning has historical roots going back decades, neither the term" deep
learning" nor the approach was popular just over five years ago, when the field was …

Small sample learning aims to learn information about object categories from a single or a
few training samples. This learning style is crucial for deep learning methods based on large …

Humans can quickly learn new visual concepts, perhaps because they can easily visualize
or imagine what novel objects look like from different views. Incorporating this ability to …

We describe an approach to learning from long-tailed, imbalanced datasets that are
prevalent in real-world settings. Here, the challenge is to learn accurate" few-shot''models for …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …