49 citations · 145 across the 13 of their papers we have counts for
14 papers
Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
Myong Chol Jung, He Zhao, Joanna Dipnall +1
Uncertainty estimation is an important research area to make deep neural networks (DNNs) more trustworthy. While extensive research on uncertainty estimation has been conducted wit…
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Myong Chol Jung, He Zhao, Joanna Dipnall +2
Uncertainty estimation is essential to make neural networks trustworthy in real-world applications. Extensive research efforts have been made to quantify and reduce predictive unce…
SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speech
Weidong Chen, Xiaofen Xing, Xiangmin Xu +2
Transformer has obtained promising results on cognitive speech signal processing field, which is of interest in various applications ranging from emotion to neurocognitive disorder…
Leveraging Information Bottleneck for Scientific Document Summarization
Jiaxin Ju, Ming Liu, Huan Yee Koh +3
This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle. Inspired by previous work which uses t…
Prototype-Guided Memory Replay for Continual Learning
Stella Ho, Ming Liu, Lan Du +2
Continual learning (CL) refers to a machine learning paradigm that learns continuously without forgetting previously acquired knowledge. Thereby, major difficulty in CL is catastro…
Learning Graph Neural Networks with Positive and Unlabeled Nodes
Man Wu, Shirui Pan, Lan Du +1
Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdep…