31 citations · 70 across the 8 of their papers we have counts for
8 papers
SAIL: Self-Augmented Graph Contrastive Learning
Lu Yu, Shichao Pei, Lizhong Ding +4
This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirica…
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS
Yong Liu, Lizhong Ding, Weiping Wang
Theoretical analysis of the divide-and-conquer based distributed learning with least square loss in the reproducing kernel Hilbert space (RKHS) have recently been explored within t…
Nearly Optimal Clustering Risk Bounds for Kernel K-Means
Yong Liu, Lizhong Ding, Weiping Wang
In this paper, we study the statistical properties of kernel -means and obtain a nearly optimal excess clustering risk bound, substantially improving the state-of-art bounds in…
Dynamically Visual Disambiguation of Keyword-based Image Search
Yazhou Yao, Zeren Sun, Fumin Shen +6
Due to the high cost of manual annotation, learning directly from the web has attracted broad attention. One issue that limits their performance is the problem of visual polysemy.…
Deep learning in bioinformatics: introduction, application, and perspective in big data era
Yu Li, Chao Huang, Lizhong Ding +3
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in…
Efficient Cross-Validation for Semi-Supervised Learning
Yong Liu, Jian Li, Guangjun Wu +2
Manifold regularization, such as laplacian regularized least squares (LapRLS) and laplacian support vector machine (LapSVM), has been widely used in semi-supervised learning, and i…
On the Decision Boundary of Deep Neural Networks
Yu Li, Lizhong Ding, Xin Gao
While deep learning models and techniques have achieved great empirical success, our understanding of the source of success in many aspects remains very limited. In an attempt to b…
SupportNet: solving catastrophic forgetting in class incremental learning with support data
Yu Li, Zhongxiao Li, Lizhong Ding +5
A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic…