Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Zou, Dongmian"'
Hyperbolic neural networks (HNNs) have been proved effective in modeling complex data structures. However, previous works mainly focused on the Poincar\'e ball model and the hyperboloid model as coordinate representations of the hyperbolic space, oft
Externí odkaz:
http://arxiv.org/abs/2410.16813
Hyperbolic representations have shown remarkable efficacy in modeling inherent hierarchies and complexities within data structures. Hyperbolic neural networks have been commonly applied for learning such representations from data, but they often fall
Externí odkaz:
http://arxiv.org/abs/2407.10495
Graph neural networks (GNNs) have become pivotal tools for processing graph-structured data, leveraging the message passing scheme as their core mechanism. However, traditional GNNs often grapple with issues such as instability, over-smoothing, and o
Externí odkaz:
http://arxiv.org/abs/2407.06988
Autor:
Gu, Jing, Zou, Dongmian
Graph anomaly detection plays a vital role for identifying abnormal instances in complex networks. Despite advancements of methodology based on deep learning in recent years, existing benchmarking approaches exhibit limitations that hinder a comprehe
Externí odkaz:
http://arxiv.org/abs/2403.04010
Publikováno v:
Neural Networks 178(2024) 106463
Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods face challenges in consistently achieving satisfactory performance and often lack interpretability, which hinders our understanding of anomaly detect
Externí odkaz:
http://arxiv.org/abs/2311.06153
Generative adversarial networks (GANs) are popular for generative tasks; however, they often require careful architecture selection, extensive empirical tuning, and are prone to mode collapse. To overcome these challenges, we propose a novel model th
Externí odkaz:
http://arxiv.org/abs/2311.01375
Autor:
Qu, Eric, Zou, Dongmian
Learning representations according to the underlying geometry is of vital importance for non-Euclidean data. Studies have revealed that the hyperbolic space can effectively embed hierarchical or tree-like data. In particular, the few past years have
Externí odkaz:
http://arxiv.org/abs/2306.08862
The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has been considered as an effective approach for throughput enhancement w
Externí odkaz:
http://arxiv.org/abs/2209.06905
We propose a novel and trainable graph unpooling layer for effective graph generation. Given a graph with features, the unpooling layer enlarges this graph and learns its desired new structure and features. Since this unpooling layer is trainable, it
Externí odkaz:
http://arxiv.org/abs/2206.01874
Publikováno v:
In Neural Networks October 2024 178
