Chengbin Hou
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View article: A review of recent artificial intelligence for traditional medicine
A review of recent artificial intelligence for traditional medicine Open
Traditional Medicine (TM) has played a crucial role in global healthcare due to its long history and holistic approach. Artificial Intelligence (AI) has emerged as a revolutionary technology, offering exceptional capabilities in areas such…
View article: Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning Open
High-resolution point clouds~(HRPCD) anomaly detection~(AD) plays a critical\nrole in precision machining and high-end equipment manufacturing. Despite\nconsiderable 3D-AD methods that have been proposed recently, they still cannot\nmeet t…
View article: <scp>DDE KG</scp>Editor: A data service system for knowledge graph construction in geoscience
<span>DDE KG</span>Editor: A data service system for knowledge graph construction in geoscience Open
Deep‐time Digital Earth (DDE) is an innovative international big science program, focusing on scientific propositions of earth evolution, changing Earth Science by coordinating global geoscience data, and sharing global geoscience knowledg…
View article: Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs
Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs Open
Node importance estimation (NIE) is the task of inferring the importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicated to knowledge graphs (KGs) f…
View article: Fossil image identification using deep learning ensembles of data augmented multiviews
Fossil image identification using deep learning ensembles of data augmented multiviews Open
Identification of fossil species is crucial to evolutionary studies. Recent advances from deep learning have shown promising prospects in fossil image identification. However, the quantity and quality of labelled fossil images are often li…
View article: Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews
Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews Open
Identification of fossil species is crucial to evolutionary studies. Recent advances from deep learning have shown promising prospects in fossil image identification. However, the quantity and quality of labeled fossil images are often lim…
View article: A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection Open
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite these progresses, how to ensure various deep graph learning …
View article: Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack Open
Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce to drug and advanced material discovery. Despite the progress, applying DGL to real-world applications fac…
View article: Towards Robust Dynamic Network Embedding
Towards Robust Dynamic Network Embedding Open
Dynamic Network Embedding (DNE) has recently drawn much attention due to the dynamic nature of many real-world networks. Comparing to a static network, a dynamic network has a unique character called the degree of changes, which can be def…
View article: Robust Dynamic Network Embedding via Ensembles
Robust Dynamic Network Embedding via Ensembles Open
Dynamic Network Embedding (DNE) has recently attracted considerable attention due to the advantage of network embedding in various fields and the dynamic nature of many real-world networks. An input dynamic network to DNE is often assumed …
View article: GloDyNE: Global Topology Preserving Dynamic Network Embedding
GloDyNE: Global Topology Preserving Dynamic Network Embedding Open
Learning low-dimensional topological representation of a network in dynamic\nenvironments is attracting much attention due to the time-evolving nature of\nmany real-world networks. The main and common objective of Dynamic Network\nEmbeddin…
View article: DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding Open
Learning topological representation of a network in dynamic environments has recently attracted considerable attention due to the time-evolving nature of many real-world networks i.e. nodes/links might be added/removed as time goes on. Dyn…
View article: Learning Topological Representation for Networks via Hierarchical Sampling
Learning Topological Representation for Networks via Hierarchical Sampling Open
The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a low-dimensional vector space, has been shown their advantag…
View article: Attributed Network Embedding for Incomplete Attributed Networks
Attributed Network Embedding for Incomplete Attributed Networks Open
Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e.g. a social network with user profiles. Attributed Network Embedding (ANE) has recently attracted considerable attention, which aims to l…