35 citations · 87 across the 12 of their papers we have counts for
16 papers
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…