45 citations · 59 across the 11 of their papers we have counts for
5 papers · 1 filter
STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models
Belhal Karimi, Jianwen Xie, Ping Li
We propose in this paper, STANLEY, a STochastic gradient ANisotropic LangEvin dYnamics, for sampling high dimensional data. With the growing efficacy and potential of Energy-Based…
Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representation
Yaxuan Zhu, Jianwen Xie, Ping Li
We propose the NeRF-LEBM, a likelihood-based top-down 3D-aware 2D image generative model that incorporates 3D representation via Neural Radiance Fields (NeRF) and 2D imaging proces…
CoopInit: Initializing Generative Adversarial Networks via Cooperative Learning
Yang Zhao, Jianwen Xie, Ping Li
Numerous research efforts have been made to stabilize the training of the Generative Adversarial Networks (GANs), such as through regularization and architecture design. However, w…
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference
Jianwen Xie, Yaxuan Zhu, Yifei Xu +2
We study a normalizing flow in the latent space of a top-down generator model, in which the normalizing flow model plays the role of the informative prior model of the generator. W…
Exploration in Model-based Reinforcement Learning with Randomized Reward
Lingxiao Wang, Ping Li
Model-based Reinforcement Learning (MBRL) has been widely adapted due to its sample efficiency. However, existing worst-case regret analysis typically requires optimistic planning,…