25 citations · 46 across the 7 of their papers we have counts for
7 papers
A Preconditioned Riemannian Gradient Descent Algorithm for Low-Rank Matrix Recovery
Fengmiao Bian, Jian-Feng Cai, Rui Zhang
The low-rank matrix recovery problem often arises in various fields, including signal processing, machine learning, and imaging science. The Riemannian gradient descent (RGD) algor…
Dirichlet-based Uncertainty Calibration for Active Domain Adaptation
Mixue Xie, Shuang Li, Rui Zhang +1
Active domain adaptation (DA) aims to maximally boost the model adaptation on a new target domain by actively selecting limited target data to annotate, whereas traditional active…
Adaptive Depth Graph Attention Networks
Jingbo Zhou, Yixuan Du, Ruqiong Zhang +1
As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely…
Performance Analysis of OMP in Super-Resolution
Yuxuan Han, Zhiyi Huang, Yang Wang +1
Given a spectrally sparse signal consisting of complex sinusoids, we consider the super-resolution problem,…
Deep Manifold Learning with Graph Mining
Xuelong Li, Ziheng Jiao, Hongyuan Zhang +1
Admittedly, Graph Convolution Network (GCN) has achieved excellent results on graph datasets such as social networks, citation networks, etc. However, softmax used as the decision…
Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems
Yixin Su, Yunxiang Zhao, Sarah Erfani +2
Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions. However, the…