35 citations · 155 across the 31 of their papers we have counts for
31 papers
Towards Generalising Neural Topical Representations
Xiaohao Yang, He Zhao, Dinh Phung +1
Topic models have evolved from conventional Bayesian probabilistic models to recent Neural Topic Models (NTMs). Although NTMs have shown promising performance when trained and test…
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
Anh Bui, Trung Le, He Zhao +3
Deep learning models, even the-state-of-the-art ones, are highly vulnerable to adversarial examples. Adversarial training is one of the most efficient methods to improve the model'…
Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
Vy Vo, Trung Le, Tung-Long Vuong +3
Estimating the parameters of a probabilistic directed graphical model from incomplete data is a long-standing challenge. This is because, in the presence of latent variables, both…
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…
Transformed Distribution Matching for Missing Value Imputation
He Zhao, Ke Sun, Amir Dezfouli +1
We study the problem of imputing missing values in a dataset, which has important applications in many domains. The key to missing value imputation is to capture the data distribut…
Vector Quantized Wasserstein Auto-Encoder
Tung-Long Vuong, Trung Le, He Zhao +4
Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks. Inspired by the sem…
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
Dandan Guo, Long Tian, He Zhao +2
Few-shot classification aims to learn a classifier to recognize unseen classes during training, where the learned model can easily become over-fitted based on the biased distributi…
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…
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Vy Vo, Trung Le, Van Nguyen +4
Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability. One flourishing approach i…
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Dandan Guo, Zhuo Li, Meixi Zheng +3
Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training s…
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
Dongsheng Wang, Dandan Guo, He Zhao +4
A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is…
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
Tuan Anh Bui, Trung Le, Quan Tran +2
It is well-known that deep neural networks (DNNs) are susceptible to adversarial attacks, exposing a severe fragility of deep learning systems. As the result, adversarial training…
Neural Attention-Aware Hierarchical Topic Model
Yuan Jin, He Zhao, Ming Liu +2
Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count inf…
Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction
He Zhao, Richard P. Wildes
Video predictive understanding encompasses a wide range of efforts that are concerned with the anticipation of the unobserved future from the current as well as historical video ob…
Interpretable Deep Feature Propagation for Early Action Recognition
He Zhao, Richard P. Wildes
Early action recognition (action prediction) from limited preliminary observations plays a critical role for streaming vision systems that demand real-time inference, as video acti…
Improved and Efficient Text Adversarial Attacks using Target Information
Mahmoud Hossam, Trung Le, He Zhao +2
There has been recently a growing interest in studying adversarial examples on natural language models in the black-box setting. These methods attack natural language classifiers b…
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…
Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning
Anh Bui, Trung Le, He Zhao +3
Contrastive learning (CL) has recently emerged as an effective approach to learning representation in a range of downstream tasks. Central to this approach is the selection of posi…
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability
Mahmoud Hossam, Trung Le, He Zhao +1
Training robust deep learning models for down-stream tasks is a critical challenge. Research has shown that down-stream models can be easily fooled with adversarial inputs that loo…
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks
He Zhao, Thanh Nguyen, Trung Le +4
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Many advers…
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
Anh Bui, Trung Le, He Zhao +4
Ensemble-based adversarial training is a principled approach to achieve robustness against adversarial attacks. An important technique of this approach is to control the transferab…
Neural Topic Model via Optimal Transport
He Zhao, Dinh Phung, Viet Huynh +2
Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it…
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models
Thanh Nguyen-Duc, He Zhao, Jianfei Cai +1
Deep learning methods usually require a large amount of training data and lack interpretability. In this paper, we propose a novel knowledge distillation and model interpretation f…
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
Jinming Zhao, Ming Liu, Longxiang Gao +5
Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains. In this pa…