Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

摘要过去几年, 卷积神经网络因其强大的建模能力引起广泛关注, 在自然语言处理,
图像识别等领域成功应用. 然而, 传统的卷积神经网络只能处理欧氏空间数据 …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

Dependency trees convey rich structural information that is proven useful for extracting
relations among entities in text. However, how to effectively make use of relevant information …