33 citations · 126 across the 24 of their papers we have counts for
5 papers · 1 filter
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
Bowen Tian, Qinliang Su, Jianxing Yu
Generative adversarial networks (GANs) are known for their strong abilities on capturing the underlying distribution of training instances. Since the seminal work of GAN, many vari…
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs
Bowen Tian, Qinliang Su, Jian Yin
The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but t…
Learning Neural Set Functions Under the Optimal Subset Oracle
Zijing Ou, Tingyang Xu, Qinliang Su +3
Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority o…
Generative Semantic Hashing Enhanced via Boltzmann Machines
Lin Zheng, Qinliang Su, Dinghan Shen +1
Generative semantic hashing is a promising technique for large-scale information retrieval thanks to its fast retrieval speed and small memory footprint. For the tractability of tr…
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck
Yang Zhao, Ping Yu, Suchismit Mahapatra +2
Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. However, the sequential text generation common pitfall with VAEs is that the model tends…