The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …
Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence
Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …
powerful modeling capabilities and have been successfully applied in natural language …
Graph neural networks: foundation, frontiers and applications
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 …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
A survey on text classification: From traditional to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Lasuie: Unifying information extraction with latent adaptive structure-aware generative language model
Universally modeling all typical information extraction tasks (UIE) with one generative
language model (GLM) has revealed great potential by the latest study, where various IE …
language model (GLM) has revealed great potential by the latest study, where various IE …
Graph learning: A survey
Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …
data. Graph data can be found in a broad spectrum of application domains such as social …
Graph neural networks for natural language processing: A survey
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 …
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Benchmarking graph neural networks
In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …
Imagine that! abstract-to-intricate text-to-image synthesis with scene graph hallucination diffusion
In this work, we investigate the task of text-to-image (T2I) synthesis under the abstract-to-
intricate setting, ie, generating intricate visual content from simple abstract text prompts …
intricate setting, ie, generating intricate visual content from simple abstract text prompts …