Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence. - Ruiqi Gao; Yang Lu 006; Junpei Zhou; Song-Chun Zhu; Ying Nian Wu (CoRR, 2017)

Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM. - Hong Song; Lei Chen 073; Ruiqi Gao; Iordachescu Ilie Mihaita Bogdan; Jian Yang 009; Shuliang Wang 001; Wentian Dong; Wenxiang Quan; Weimin Dang; Xin Yu (BMC Medical Informatics Decis. Mak., 2017)

Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching. - Jianwen Xie; Yang Lu 006; Ruiqi Gao; Ying Nian Wu (AAAI, 2018)

Learning Generative ConvNets via Multi-Grid Modeling and Sampling. - Ruiqi Gao; Yang Lu 006; Junpei Zhou; Song-Chun Zhu; Ying Nian Wu (CVPR, 2018)

Learning Descriptor Networks for 3D Shape Synthesis and Analysis. - Jianwen Xie; Zilong Zheng; Ruiqi Gao; Wenguan Wang; Song-Chun Zhu; Ying Nian Wu (CVPR, 2018)

Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry. - Xianglei Xing; Ruiqi Gao; Tian Han 001; Song-Chun Zhu; Ying Nian Wu (CoRR, 2018)

A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models. - Ying Nian Wu; Ruiqi Gao; Tian Han 001; Song-Chun Zhu (CoRR, 2018)

Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion. - Ruiqi Gao; Jianwen Xie; Song-Chun Zhu; Ying Nian Wu (CoRR, 2018)

Learning Dynamic Generator Model by Alternating Back-Propagation through Time. - Jianwen Xie; Ruiqi Gao; Zilong Zheng; Song-Chun Zhu; Ying Nian Wu (AAAI, 2019)

Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network. - Xianglei Xing; Tian Han 001; Ruiqi Gao; Song-Chun Zhu; Ying Nian Wu (CVPR, 2019)

Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion. - Ruiqi Gao; Jianwen Xie; Song-Chun Zhu; Ying Nian Wu (ICLR (Poster), 2019)

Multimodal 3D Convolutional Neural Networks for Classification of Brain Disease Using Structural MR and FDG-PET Images. - Kun Han; Haiwei Pan; Ruiqi Gao; Jieyao Yu; Bin Yang (ICPCSEE (1), 2019)

Convergence of Adversarial Training in Overparametrized Neural Networks. - Ruiqi Gao; Tianle Cai; Haochuan Li; Cho-Jui Hsieh; Liwei Wang 001; Jason D. Lee (NeurIPS, 2019)

Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1. - Ruiqi Gao; Jianwen Xie; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems. - Tianle Cai; Ruiqi Gao; Jikai Hou; Siyu Chen; Dong Wang; Di He 001; Zhihua Zhang; Liwei Wang 001 (CoRR, 2019)

Convergence of Adversarial Training in Overparametrized Networks. - Ruiqi Gao; Tianle Cai; Haochuan Li; Liwei Wang 001; Cho-Jui Hsieh; Jason D. Lee (CoRR, 2019)

Representation Learning: A Statistical Perspective. - Jianwen Xie; Ruiqi Gao; Erik Nijkamp; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns. - Jianwen Xie; Ruiqi Gao; Zilong Zheng; Song-Chun Zhu; Ying Nian Wu (AAAI, 2020)

Flow Contrastive Estimation of Energy-Based Models. - Ruiqi Gao; Erik Nijkamp; Diederik P. Kingma; Zhen Xu 006; Andrew M. Dai; Ying Nian Wu (CVPR, 2020)

Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot. - Jingtong Su; Yihang Chen; Tianle Cai; Tianhao Wu 002; Ruiqi Gao; Liwei Wang 001; Jason D. Lee (NeurIPS, 2020)

Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC. - Erik Nijkamp; Ruiqi Gao; Pavel Sountsov; Srinivas Vasudevan; Bo Pang 004; Song-Chun Zhu; Ying Nian Wu (CoRR, 2020)

A Representational Model of Grid Cells Based on Matrix Lie Algebras. - Ruiqi Gao; Jianwen Xie; Song-Chun Zhu; Ying Nian Wu (CoRR, 2020)

Cooperative Training of Descriptor and Generator Networks. - Jianwen Xie; Yang Lu 006; Ruiqi Gao; Song-Chun Zhu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis. - Yaxuan Zhu; Ruiqi Gao; Siyuan Huang 001; Song-Chun Zhu; Ying Nian Wu (CVPR, 2021)

Learning Energy-Based Models by Diffusion Recovery Likelihood. - Ruiqi Gao; Yang Song 011; Ben Poole; Ying Nian Wu; Diederik P. Kingma (ICLR, 2021)

A Theory of Label Propagation for Subpopulation Shift. - Tianle Cai; Ruiqi Gao; Jason D. Lee; Qi Lei (ICML, 2021)

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling. - Ruiqi Gao; Jianwen Xie; Xue-Xin Wei; Song-Chun Zhu; Ying Nian Wu (NeurIPS, 2021)

Effective Learning of Descriptive and Generator Models and Learning Representations for Grid Cells and V1 Cells. - Ruiqi Gao (2021)

Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion. - Ruiqi Gao; Jianwen Xie; Siyuan Huang 001; Yufan Ren; Song-Chun Zhu; Ying Nian Wu (AAAI, 2022)

MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC. - Erik Nijkamp; Ruiqi Gao; Pavel Sountsov; Srinivas Vasudevan; Bo Pang 004; Song-Chun Zhu; Ying Nian Wu (ICLR, 2022)

Latent Diffusion Energy-Based Model for Interpretable Text Modelling. - Peiyu Yu; Sirui Xie; Xiaojian Ma 001; Baoxiong Jia; Bo Pang 004; Ruiqi Gao; Yixin Zhu 001; Song-Chun Zhu; Ying Nian Wu (ICML, 2022)

Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells. - Dehong Xu; Ruiqi Gao; Wenhao Zhang 002; Xue-Xin Wei; Ying Nian Wu (NeurReps, 2022)

Latent Diffusion Energy-Based Model for Interpretable Text Modeling. - Peiyu Yu; Sirui Xie; Xiaojian Ma 001; Baoxiong Jia; Bo Pang 004; Ruiqi Gao; Yixin Zhu 001; Song-Chun Zhu; Ying Nian Wu (CoRR, 2022)

Imagen Video: High Definition Video Generation with Diffusion Models. - Jonathan Ho; William Chan; Chitwan Saharia; Jay Whang; Ruiqi Gao; Alexey A. Gritsenko; Diederik P. Kingma; Ben Poole; Mohammad Norouzi 002; David J. Fleet; Tim Salimans (CoRR, 2022)

On Distillation of Guided Diffusion Models. - Chenlin Meng; Ruiqi Gao; Diederik P. Kingma; Stefano Ermon; Jonathan Ho; Tim Salimans (CoRR, 2022)

Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis. - Jianwen Xie; Zilong Zheng; Ruiqi Gao; Wenguan Wang; Song-Chun Zhu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry. - Xianglei Xing; Ruiqi Gao; Tian Han 001; Song-Chun Zhu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Weightings in Incremental ADCs: A Tutorial Review. - Ruiqi Gao; Mingqiang Guo; Sai-Weng Sin; Liang Qi 002; Biao Wang; Guoxing Wang; Rui Paulo Martins (CICC, 2023)

On Distillation of Guided Diffusion Models. - Chenlin Meng; Robin Rombach; Ruiqi Gao; Diederik P. Kingma; Stefano Ermon; Jonathan Ho; Tim Salimans (CVPR, 2023)

Compiling Parallel Symbolic Execution with Continuations. - Guannan Wei 001; Songlin Jia; Ruiqi Gao; Haotian Deng 001; Shangyin Tan; Oliver Bracevac; Tiark Rompf (ICSE, 2023)

Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation. - Diederik P. Kingma; Ruiqi Gao (NeurIPS, 2023)

Learning Energy-Based Prior Model with Diffusion-Amortized MCMC. - Peiyu Yu; Yaxuan Zhu; Sirui Xie; Xiaojian Ma 001; Ruiqi Gao; Song-Chun Zhu; Ying Nian Wu (NeurIPS, 2023)

Understanding the Diffusion Objective as a Weighted Integral of ELBOs. - Diederik P. Kingma; Ruiqi Gao (CoRR, 2023)

Conformal Normalization in Recurrent Neural Network of Grid Cells. - Dehong Xu; Ruiqi Gao; Wenhao Zhang 002; Xue-Xin Wei; Ying Nian Wu (CoRR, 2023)

ReconFusion: 3D Reconstruction with Diffusion Priors. - Rundi Wu; Ben Mildenhall; Philipp Henzler; Keunhong Park; Ruiqi Gao; Daniel Watson; Pratul P. Srinivasan; Dor Verbin; Jonathan T. Barron; Ben Poole; Aleksander Holynski (CVPR, 2024)

MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Space. - Armand Comas Massague; Di Qiu; Menglei Chai; Marcel C. Bühler; Amit Raj; Ruiqi Gao; Qiangeng Xu; Mark Matthews; Paulo F. U. Gotardo; Sergio Orts-Escolano; Thabo Beeler (ECCV (66), 2024)

Large Language Models are Limited in Out-of-Context Knowledge Reasoning. - Peng Hu; Changjiang Gao; Ruiqi Gao; Jiajun Chen 001; Shujian Huang (EMNLP (Findings), 2024)

Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood. - Yaxuan Zhu; Jianwen Xie; Ying Nian Wu; Ruiqi Gao (ICLR, 2024)

Generative Hierarchical Materials Search. - Sherry Yang 001; Simon L. Batzner; Ruiqi Gao; Muratahan Aykol; Alexander L. Gaunt; Brendan McMorrow; Danilo Jimenez Rezende; Dale Schuurmans; Igor Mordatch; Ekin Dogus Cubuk (NeurIPS, 2024)

CAT3D: Create Anything in 3D with Multi-View Diffusion Models. - Ruiqi Gao; Aleksander Holynski; Philipp Henzler; Arthur Brussee; Ricardo Martin-Brualla; Pratul P. Srinivasan; Jonathan T. Barron; Ben Poole (NeurIPS, 2024)

EM Distillation for One-step Diffusion Models. - Sirui Xie; Zhisheng Xiao; Diederik P. Kingma; Tingbo Hou; Ying Nian Wu; Kevin P. Murphy; Tim Salimans; Ben Poole; Ruiqi Gao (NeurIPS, 2024)

MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Space. - Armand Comas Massague; Di Qiu; Menglei Chai; Marcel C. Bühler; Amit Raj; Ruiqi Gao; Qiangeng Xu; Mark Matthews; Paulo F. U. Gotardo; Octavia I. Camps; Sergio Orts-Escolano; Thabo Beeler (CoRR, 2024)

CoLay: Controllable Layout Generation through Multi-conditional Latent Diffusion. - Chin-Yi Cheng; Ruiqi Gao; Forrest Huang; Yang Li 058 (CoRR, 2024)

Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. - Peiyu Yu; Dinghuai Zhang; Hengzhi He; Xiaojian Ma 001; Ruiyao Miao; Yifan Lu; Yasi Zhang; Deqian Kong; Ruiqi Gao; Jianwen Xie; Guang Cheng 003; Ying Nian Wu (CoRR, 2024)

An Investigation of Conformal Isometry Hypothesis for Grid Cells. - Dehong Xu; Ruiqi Gao; Wenhao Zhang 002; Xue-Xin Wei; Ying Nian Wu (CoRR, 2024)

Limited Out-of-Context Knowledge Reasoning in Large Language Models. - Peng Hu; Changjiang Gao; Ruiqi Gao; Jiajun Chen 001; Shujian Huang (CoRR, 2024)

Think Twice Before You Act: Improving Inverse Problem Solving With MCMC. - Yaxuan Zhu; Zehao Dou; Haoxin Zheng; Yasi Zhang; Ying Nian Wu; Ruiqi Gao (CoRR, 2024)

Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion. - Emiel Hoogeboom; Thomas Mensink; Jonathan Heek; Kay Lamerigts; Ruiqi Gao; Tim Salimans (CoRR, 2024)

Simpler Diffusion: 1.5 FID on ImageNet512 with Pixel-space Diffusion. - Emiel Hoogeboom; Thomas Mensink; Jonathan Heek; Kay Lamerigts; Ruiqi Gao; Tim Salimans (CVPR, 2025)

SimVS: Simulating World Inconsistencies for Robust View Synthesis. - Alex Trevithick; Roni Paiss; Philipp Henzler; Dor Verbin; Rundi Wu; Hadi Alzayer; Ruiqi Gao; Ben Poole; Jonathan T. Barron; Aleksander Holynski; Ravi Ramamoorthi; Pratul P. Srinivasan (CVPR, 2025)

CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models. - Rundi Wu; Ruiqi Gao; Ben Poole; Alex Trevithick; Changxi Zheng; Jonathan T. Barron; Aleksander Holynski (CVPR, 2025)

SceneCrafter: Controllable Multi-View Driving Scene Editing. - Zehao Zhu; Yuliang Zou; Chiyu Max Jiang; Bo Sun; Vincent Casser; Xiukun Huang; Jiahao Wang; Zhenpei Yang; Ruiqi Gao; Leonidas J. Guibas; Mingxing Tan; Dragomir Anguelov (CVPR, 2025)

On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding. - Dehong Xu; Ruiqi Gao; Wenhao Zhang 002; Xue-Xin Wei; Ying Nian Wu (ICLR, 2025)

Bolt3D: Generating 3D Scenes in Seconds. - Stanislaw Szymanowicz; Jason Y. Zhang 004; Pratul P. Srinivasan; Ruiqi Gao; Arthur Brussee; Aleksander Holynski; Ricardo Martin-Brualla; Jonathan T. Barron; Philipp Henzler (CoRR, 2025)