Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Wang, Beilun"'
Autor:
Wang, Yuchen, Zhang, Jinghui, Huang, Zhengjie, Li, Weibin, Feng, Shikun, Ma, Ziheng, Sun, Yu, Yu, Dianhai, Dong, Fang, Jin, Jiahui, Wang, Beilun, Luo, Junzhou
Node classification is a substantial problem in graph-based fraud detection. Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising, currently most GNN-based fraud detectors fail to generalize to the low ho
Externí odkaz:
http://arxiv.org/abs/2302.10407
Autor:
Zhang, Jiaqi, Wang, Beilun
Because tensor data appear more and more frequently in various scientific researches and real-world applications, analyzing the relationship between tensor features and the univariate outcome becomes an elementary task in many fields. To solve this t
Externí odkaz:
http://arxiv.org/abs/1912.01450
Recently, tensor data (or multidimensional array) have been generated in many modern applications, such as functional magnetic resonance imaging (fMRI) in neuroscience and videos in video analysis. Many efforts are made in recent years to predict the
Externí odkaz:
http://arxiv.org/abs/1911.12965
Publikováno v:
International Conference on Machine Learning. 2018
We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroimaging applications. Previous joint sGGM estimators either fail to use
Externí odkaz:
http://arxiv.org/abs/1806.00548
Publikováno v:
In Knowledge-Based Systems 5 December 2022 257
Publikováno v:
In Neurocomputing 1 December 2022 514:39-57
We focus on the problem of estimating the change in the dependency structures of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth optimization. We p
Externí odkaz:
http://arxiv.org/abs/1710.11223
Determining functional brain connectivity is crucial to understanding the brain and neural differences underlying disorders such as autism. Recent studies have used Gaussian graphical models to learn brain connectivity via statistical dependencies ac
Externí odkaz:
http://arxiv.org/abs/1709.04090
Autor:
Singh, Ritambhara, Sekhon, Arshdeep, Kowsari, Kamran, Lanchantin, Jack, Wang, Beilun, Qi, Yanjun
String Kernel (SK) techniques, especially those using gapped $k$-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dic
Externí odkaz:
http://arxiv.org/abs/1704.07468
Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensiti
Externí odkaz:
http://arxiv.org/abs/1702.06763