124 citations · 279 across the 11 of their papers we have counts for
11 papers
InverseNet: Solving Inverse Problems with Splitting Networks
Kai Fan, Qi Wei, Wenlin Wang +2
We propose a new method that uses deep learning techniques to solve the inverse problems. The inverse problem is cast in the form of learning an end-to-end mapping from observed da…
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…
An inner-loop free solution to inverse problems using deep neural networks
Qi Wei, Kai Fan, Lawrence Carin +1
We propose a new method that uses deep learning techniques to accelerate the popular alternating direction method of multipliers (ADMM) solution for inverse problems. The ADMM upda…
Adversarial Feature Matching for Text Generation
Yizhe Zhang, Zhe Gan, Kai Fan +4
The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with…
Unifying the Stochastic Spectral Descent for Restricted Boltzmann Machines with Bernoulli or Gaussian Inputs
Kai Fan
Stochastic gradient descent based algorithms are typically used as the general optimization tools for most deep learning models. A Restricted Boltzmann Machine (RBM) is a probabili…
Boosting Variational Inference
Fangjian Guo, Xiangyu Wang, Kai Fan +2
Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closes…