346 citations · 622 across the 32 of their papers we have counts for
32 papers
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…
On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding
Dehong Xu, Ruiqi Gao, Wen-Hao Zhang +2
This paper investigates the conformal isometry hypothesis as a potential explanation for the hexagonal periodic patterns in grid cell response maps. We posit that grid cell activit…
EM Distillation for One-step Diffusion Models
Sirui Xie, Zhisheng Xiao, Diederik P Kingma +6
While diffusion models can learn complex distributions, sampling requires a computationally expensive iterative process. Existing distillation methods enable efficient sampling, bu…
"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood
Peiyu Yu, Dinghuai Zhang, Hengzhi He +10
Noise Contrastive Estimation (NCE) has fueled major breakthroughs in representation learning and generative modeling. Yet a long-standing challenge remains: accurately estimating r…
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
Peiyu Yu, Yaxuan Zhu, Sirui Xie +4
Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in the field of generative modeling due to its flexibility in the formulati…
Emergence of Grid-like Representations by Training Recurrent Networks with Conformal Normalization
Dehong Xu, Ruiqi Gao, Wen-Hao Zhang +2
Grid cells in the entorhinal cortex of mammalian brains exhibit striking hexagon grid firing patterns in their response maps as the animal (e.g., a rat) navigates in a 2D open envi…
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…
Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation
Diederik P. Kingma, Ruiqi Gao
To achieve the highest perceptual quality, state-of-the-art diffusion models are optimized with objectives that typically look very different from the maximum likelihood and the Ev…
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells
Dehong Xu, Ruiqi Gao, Wen-Hao Zhang +2
The activity of the grid cell population in the medial entorhinal cortex (MEC) of the mammalian brain forms a vector representation of the self-position of the animal. Recurrent ne…
Imagen Video: High Definition Video Generation with Diffusion Models
Jonathan Ho, William Chan, Chitwan Saharia +8
We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models. Given a text prompt, Imagen Video generates high definition videos…
On Distillation of Guided Diffusion Models
Chenlin Meng, Robin Rombach, Ruiqi Gao +4
Classifier-free guided diffusion models have recently been shown to be highly effective at high-resolution image generation, and they have been widely used in large-scale diffusion…
Latent Diffusion Energy-Based Model for Interpretable Text Modeling
Peiyu Yu, Sirui Xie, Xiaojian Ma +6
Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling. Fueled by its flexibility in the formulation and st…
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis
Yaxuan Zhu, Ruiqi Gao, Siyuan Huang +2
How to effectively represent camera pose is an essential problem in 3D computer vision, especially in tasks such as camera pose regression and novel view synthesis. Traditionally,…
A Theory of Label Propagation for Subpopulation Shift
Tianle Cai, Ruiqi Gao, Jason D. Lee +1
One of the central problems in machine learning is domain adaptation. Unlike past theoretical work, we consider a new model for subpopulation shift in the input or representation s…
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
Jianwen Xie, Zilong Zheng, Ruiqi Gao +3
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it beco…
Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Song, Ben Poole +2
While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on di…
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Jingtong Su, Yihang Chen, Tianle Cai +4
Network pruning is a method for reducing test-time computational resource requirements with minimal performance degradation. Conventional wisdom of pruning algorithms suggests that…
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling
Ruiqi Gao, Jianwen Xie, Xue-Xin Wei +2
Understanding how grid cells perform path integration calculations remains a fundamental problem. In this paper, we conduct theoretical analysis of a general representation model o…
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…
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns
Jianwen Xie, Ruiqi Gao, Zilong Zheng +2
Dynamic patterns are characterized by complex spatial and motion patterns. Understanding dynamic patterns requires a disentangled representational model that separates the factoria…
Convergence of Adversarial Training in Overparametrized Neural Networks
Ruiqi Gao, Tianle Cai, Haochuan Li +3
Neural networks are vulnerable to adversarial examples, i.e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network. Adversarial tr…
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems
Tianle Cai, Ruiqi Gao, Jikai Hou +5
First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better con…