346 citations · 622 across the 32 of their papers we have counts for
10 papers · 1 filter
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC
Yaxuan Zhu, Zehao Dou, Haoxin Zheng +3
Recent studies demonstrate that diffusion models can serve as a strong prior for solving inverse problems. A prominent example is Diffusion Posterior Sampling (DPS), which approxim…
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood
Yaxuan Zhu, Jianwen Xie, Yingnian Wu +1
Training energy-based models (EBMs) on high-dimensional data can be both challenging and time-consuming, and there exists a noticeable gap in sample quality between EBMs and other…
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov +4
Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data sp…
Flow Contrastive Estimation of Energy-Based Models
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma +3
This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial…
Representation Learning: A Statistical Perspective
Jianwen Xie, Ruiqi Gao, Erik Nijkamp +2
Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and…
Learning Dynamic Generator Model by Alternating Back-Propagation Through Time
Jianwen Xie, Ruiqi Gao, Zilong Zheng +2
This paper studies the dynamic generator model for spatial-temporal processes such as dynamic textures and action sequences in video data. In this model, each time frame of the vid…
Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion
Ruiqi Gao, Jianwen Xie, Song-Chun Zhu +1
This paper proposes a representational model for grid cells. In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or…
A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models
Ying Nian Wu, Ruiqi Gao, Tian Han +1
The pattern theory of Grenander is a mathematical framework where patterns are represented by probability models on random variables of algebraic structures. In this paper, we revi…
Learning Energy-Based Models as Generative ConvNets via Multi-grid Modeling and Sampling
Ruiqi Gao, Yang Lu, Junpei Zhou +2
This paper proposes a multi-grid method for learning energy-based generative ConvNet models of images. For each grid, we learn an energy-based probabilistic model where the energy…
Cooperative Training of Descriptor and Generator Networks
Jianwen Xie, Yang Lu, Ruiqi Gao +2
This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The…