Search Results for author: Ruiqi Gao

Found 29 papers, 11 papers with code

Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood

no code implementations10 Sep 2023 Yaxuan Zhu, Jianwen Xie, YingNian Wu, Ruiqi Gao

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 generative frameworks like GANs and diffusion models.

Image Inpainting Out-of-Distribution Detection

Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells

1 code implementation6 Oct 2022 Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei, Ying Nian Wu

Recurrent neural networks have been proposed to explain the properties of the grid cells by updating the neural activity vector based on the velocity input of the animal.

On Distillation of Guided Diffusion Models

2 code implementations CVPR 2023 Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans

For standard diffusion models trained on the pixel-space, our approach is able to generate images visually comparable to that of the original model using as few as 4 sampling steps on ImageNet 64x64 and CIFAR-10, achieving FID/IS scores comparable to that of the original model while being up to 256 times faster to sample from.

Denoising Image Generation +1

Latent Diffusion Energy-Based Model for Interpretable Text Modeling

2 code implementations13 Jun 2022 Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu

Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling.

MCMC Should Mix: Learning Energy-Based Model with Flow-Based Backbone

no code implementations ICLR 2022 Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usually parametrized by deep network, is highly multi-modal in the data space.

Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis

1 code implementation CVPR 2021 Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Ying Nian Wu

Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose.

Novel View Synthesis regression

A Theory of Label Propagation for Subpopulation Shift

no code implementations22 Feb 2021 Tianle Cai, Ruiqi Gao, Jason D. Lee, Qi Lei

In this work, we propose a provably effective framework for domain adaptation based on label propagation.

Domain Adaptation Generalization Bounds

Learning Energy-Based Models by Diffusion Recovery Likelihood

2 code implementations ICLR 2021 Ruiqi Gao, Yang song, Ben Poole, Ying Nian Wu, Diederik P. Kingma

Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions of a dataset.

Image Generation

A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras

no code implementations28 Sep 2020 Ruiqi Gao, Jianwen Xie, Xue-Xin Wei, Song-Chun Zhu, Ying Nian Wu

The grid cells in the mammalian medial entorhinal cortex exhibit striking hexagon firing patterns when the agent navigates in the open field.

Position

Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot

1 code implementation NeurIPS 2020 Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Li-Wei Wang, Jason D. Lee

In this paper, we conduct sanity checks for the above beliefs on several recent unstructured pruning methods and surprisingly find that: (1) A set of methods which aims to find good subnetworks of the randomly-initialized network (which we call "initial tickets"), hardly exploits any information from the training data; (2) For the pruned networks obtained by these methods, randomly changing the preserved weights in each layer, while keeping the total number of preserved weights unchanged per layer, does not affect the final performance.

Network Pruning

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling

1 code implementation NeurIPS 2021 Ruiqi Gao, Jianwen Xie, Xue-Xin Wei, Song-Chun Zhu, Ying Nian Wu

In this paper, we conduct theoretical analysis of a general representation model of path integration by grid cells, where the 2D self-position is encoded as a higher dimensional vector, and the 2D self-motion is represented by a general transformation of the vector.

Dimensionality Reduction Position

MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC

no code implementations12 Jun 2020 Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm.

Convergence of Adversarial Training in Overparametrized Neural Networks

no code implementations NeurIPS 2019 Ruiqi Gao, Tianle Cai, Haochuan Li, Li-Wei Wang, Cho-Jui Hsieh, Jason D. Lee

Neural networks are vulnerable to adversarial examples, i. e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network.

Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

no code implementations28 May 2019 Tianle Cai, Ruiqi Gao, Jikai Hou, Siyu Chen, Dong Wang, Di He, Zhihua Zhang, Li-Wei Wang

First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks.

regression Second-order methods

Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion

no code implementations24 Jan 2019 Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-Chun Zhu, Ying Nian Wu

This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1).

Optical Flow Estimation

Learning Dynamic Generator Model by Alternating Back-Propagation Through Time

no code implementations27 Dec 2018 Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu

The non-linear transformation of this transition model can be parametrized by a feedforward neural network.

Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion

1 code implementation ICLR 2019 Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu

In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or displacement of the agent is represented by a matrix that transforms the vector.

Position

A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models

no code implementations9 Oct 2018 Ying Nian Wu, Ruiqi Gao, Tian Han, Song-Chun Zhu

In this paper, we review three families of probability models, namely, the discriminative models, the descriptive models, and the generative models.

Descriptive

Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry

2 code implementations16 Jun 2018 Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu

We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner.

Disentanglement Transfer Learning

Learning Descriptor Networks for 3D Shape Synthesis and Analysis

1 code implementation CVPR 2018 Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu

This paper proposes a 3D shape descriptor network, which is a deep convolutional energy-based model, for modeling volumetric shape patterns.

Object

Learning Energy-Based Models as Generative ConvNets via Multi-grid Modeling and Sampling

no code implementations CVPR 2018 Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu

Within each iteration of our learning algorithm, for each observed training image, we generate synthesized images at multiple grids by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of the training image.

Cooperative Training of Descriptor and Generator Networks

no code implementations29 Sep 2016 Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu

Specifically, within each iteration of the cooperative learning algorithm, the generator model generates initial synthesized examples to initialize a finite-step MCMC that samples and trains the energy-based descriptor model.

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