463 citations · 5.1k across the 190 of their papers we have counts for
21 papers · 1 filter
On Connecting Stochastic Gradient MCMC and Differential Privacy
Bai Li, Changyou Chen, Hao Liu +1
Significant success has been realized recently on applying machine learning to real-world applications. There have also been corresponding concerns on the privacy of training data,…
Topic Compositional Neural Language Model
Wenlin Wang, Zhe Gan, Wenqi Wang +5
We propose a Topic Compositional Neural Language Model (TCNLM), a novel method designed to simultaneously capture both the global semantic meaning and the local word ordering struc…
Zero-Shot Learning via Class-Conditioned Deep Generative Models
Wenlin Wang, Yunchen Pu, Vinay Kumar Verma +5
We present a deep generative model for learning to predict classes not seen at training time. Unlike most existing methods for this problem, that represent each class as a point (v…
Adversarial Symmetric Variational Autoencoder
Yunchen Pu, Weiyao Wang, Ricardo Henao +4
A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: () from observed data fed thr…
Symmetric Variational Autoencoder and Connections to Adversarial Learning
Liqun Chen, Shuyang Dai, Yunchen Pu +3
A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sV…
Benefits from Superposed Hawkes Processes
Hongteng Xu, Dixin Luo, Xu Chen +1
The superposition of temporal point processes has been studied for many years, although the usefulness of such models for practical applications has not be fully developed. We inve…