2 citations · 5 across the 5 of their papers we have counts for
7 papers · 1 filter
LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning
Outongyi Lv, Bingxin Zhou
Modern reinforcement learning (RL) can be categorized into online and offline variants. As a pivotal aspect of both online and offline RL, current research on the Bellman equation…
Embedding Graphs on Grassmann Manifold
Bingxin Zhou, Xuebin Zheng, Yu Guang Wang +2
Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction. Given the non-Euclidean structural…
Graph Denoising with Framelet Regularizer
Bingxin Zhou, Ruikun Li, Xuebin Zheng +2
As graph data collected from the real world is merely noise-free, a practical representation of graphs should be robust to noise. Existing research usually focuses on feature smoot…
How Framelets Enhance Graph Neural Networks
Xuebin Zheng, Bingxin Zhou, Junbin Gao +4
This paper presents a new approach for assembling graph neural networks based on framelet transforms. The latter provides a multi-scale representation for graph-structured data. We…
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
Xuebin Zheng, Bingxin Zhou, Ming Li +2
Graph Neural Networks (GNNs) have recently caught great attention and achieved significant progress in graph-level applications. In this paper, we propose a framework for graph neu…
On the Trend-corrected Variant of Adaptive Stochastic Optimization Methods
Bingxin Zhou, Xuebin Zheng, Junbin Gao
Adam-type optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning…