173 citations · 204 across the 5 of their papers we have counts for
6 papers
Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders
Yifei Yang, Shibing Xiang, Ruixiang Zhang
Autoencoder and its variants have been widely applicated in anomaly detection.The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, h…
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro
Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning. In this paper, we…
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
Tong Che, Xiaofeng Liu, Site Li +4
AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably ver…
Perceptual Generative Autoencoders
Zijun Zhang, Ruixiang Zhang, Zongpeng Li +2
Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimension of data can be much lower than the ambient dim…
Understanding Hidden Memories of Recurrent Neural Networks
Yao Ming, Shaozu Cao, Ruixiang Zhang +4
Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, t…
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Tong Che, Yanran Li, Ruixiang Zhang +4
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather…