22 citations · 36 across the 7 of their papers we have counts for
5 papers · 1 filter
Co-Learning Bayesian Optimization
Zhendong Guo, Yew-Soon Ong, Tiantian He +1
Bayesian optimization (BO) is well known to be sample-efficient for solving black-box problems. However, the BO algorithms can sometimes get stuck in suboptimal solutions even with…
Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning
Tiantian He, Haicang Zhou, Yew-Soon Ong +1
Graph attention networks (GATs) are powerful tools for analyzing graph data from various real-world scenarios. To learn representations for downstream tasks, GATs generally attend…
Exploring Linear Feature Disentanglement For Neural Networks
Tiantian He, Zhibin Li, Yongshun Gong +3
Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs). Due to the complex non-linear characteristic of samples, the o…
Learning Conjoint Attentions for Graph Neural Nets
Tiantian He, Yew-Soon Ong, Lu Bai
In this paper, we present Conjoint Attentions (CAs), a class of novel learning-to-attend strategies for graph neural networks (GNNs). Besides considering the layer-wise node featur…
A Multi-Task Gradient Descent Method for Multi-Label Learning
Lu Bai, Yew-Soon Ong, Tiantian He +1
Multi-label learning studies the problem where an instance is associated with a set of labels. By treating single-label learning problem as one task, the multi-label learning probl…