35 citations · 51 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
Stratified Sampling for Extreme Multi-Label Data
Maximillian Merrillees, Lan Du
Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data. Yet, there is no method for effectively generating stratified partitions of XML d…
Topic Modelling Meets Deep Neural Networks: A Survey
He Zhao, Dinh Phung, Viet Huynh +3
Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popul…
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer
Liyuan Sun, Jianping Gou, Baosheng Yu +2
Knowledge distillation (KD), as an efficient and effective model compression technique, has been receiving considerable attention in deep learning. The key to its success is to tra…
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Jueqing Lu, Lan Du, Ming Liu +1
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training sam…
Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data
Yuan Jin, Ming Liu, Yunfeng Li +4
Non-negative tensor factorization models enable predictive analysis on count data. Among them, Bayesian Poisson-Gamma models can derive full posterior distributions of latent facto…