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2020 – today
- 2024
[c27]Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, Aleksander Holynski:
ReconFusion: 3D Reconstruction with Diffusion Priors. CVPR 2024: 21551-21561- [c26]Peng Hu, Changjiang Gao, Ruiqi Gao, Jiajun Chen, Shujian Huang:
Large Language Models are Limited in Out-of-Context Knowledge Reasoning. EMNLP (Findings) 2024: 3144-3155 - [c25]Yaxuan Zhu, Jianwen Xie, Ying Nian Wu, Ruiqi Gao:
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood. ICLR 2024 - [i37]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:
MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Space. CoRR abs/2404.01296 (2024) - [i36]Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, Ben Poole:
CAT3D: Create Anything in 3D with Multi-View Diffusion Models. CoRR abs/2405.10314 (2024) - [i35]Chin-Yi Cheng, Ruiqi Gao, Forrest Huang, Yang Li:
CoLay: Controllable Layout Generation through Multi-conditional Latent Diffusion. CoRR abs/2405.13045 (2024) - [i34]Peiyu Yu, Dinghuai Zhang, Hengzhi He, Xiaojian Ma, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu:
Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. CoRR abs/2405.16730 (2024) - [i33]Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou, Ying Nian Wu, Kevin Patrick Murphy, Tim Salimans, Ben Poole, Ruiqi Gao:
EM Distillation for One-step Diffusion Models. CoRR abs/2405.16852 (2024) - [i32]Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei, Ying Nian Wu:
An Investigation of Conformal Isometry Hypothesis for Grid Cells. CoRR abs/2405.16865 (2024) - [i31]Peng Hu, Changjiang Gao, Ruiqi Gao, Jiajun Chen, Shujian Huang:
Limited Out-of-Context Knowledge Reasoning in Large Language Models. CoRR abs/2406.07393 (2024) - [i30]Sherry Yang, Simon L. Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch, Ekin D. Cubuk:
Generative Hierarchical Materials Search. CoRR abs/2409.06762 (2024) - [i29]Yaxuan Zhu, Zehao Dou, Haoxin Zheng, Yasi Zhang, Ying Nian Wu, Ruiqi Gao:
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC. CoRR abs/2409.08551 (2024) - 2023
- [c24]Ruiqi Gao, Mingqiang Guo
, Sai-Weng Sin, Liang Qi, Biao Wang, Guoxing Wang, Rui Paulo Martins:
Weightings in Incremental ADCs: A Tutorial Review. CICC 2023: 1-8 - [c23]Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans:
On Distillation of Guided Diffusion Models. CVPR 2023: 14297-14306 - [c22]Guannan Wei, Songlin Jia, Ruiqi Gao, Haotian Deng, Shangyin Tan, Oliver Bracevac, Tiark Rompf:
Compiling Parallel Symbolic Execution with Continuations. ICSE 2023: 1316-1328 - [c21]Diederik P. Kingma, Ruiqi Gao:
Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation. NeurIPS 2023 - [c20]Peiyu Yu, Yaxuan Zhu, Sirui Xie, Xiaojian Ma, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu:
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC. NeurIPS 2023 - [i28]Diederik P. Kingma, Ruiqi Gao:
Understanding the Diffusion Objective as a Weighted Integral of ELBOs. CoRR abs/2303.00848 (2023) - [i27]Yaxuan Zhu, Jianwen Xie, Yingnian Wu, Ruiqi Gao:
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood. CoRR abs/2309.05153 (2023) - [i26]Peiyu Yu, Yaxuan Zhu, Sirui Xie, Xiaojian Ma, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu:
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC. CoRR abs/2310.03218 (2023) - [i25]Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei, Ying Nian Wu:
Conformal Normalization in Recurrent Neural Network of Grid Cells. CoRR abs/2310.19192 (2023) - [i24]Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, Aleksander Holynski:
ReconFusion: 3D Reconstruction with Diffusion Priors. CoRR abs/2312.02981 (2023) - 2022
- [j4]Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu:
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry. IEEE Trans. Pattern Anal. Mach. Intell. 44(3): 1162-1179 (2022) - [j3]Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu:
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 44(5): 2468-2484 (2022) - [c19]Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-Chun Zhu, Ying Nian Wu:
Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion. AAAI 2022: 6674-6684 - [c18]Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC. ICLR 2022 - [c17]Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu:
Latent Diffusion Energy-Based Model for Interpretable Text Modelling. ICML 2022: 25702-25720 - [c16]Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei, Ying Nian Wu:
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells. NeurReps 2022: 370-387 - [i23]Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu:
Latent Diffusion Energy-Based Model for Interpretable Text Modeling. CoRR abs/2206.05895 (2022) - [i22]Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey A. Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, Tim Salimans:
Imagen Video: High Definition Video Generation with Diffusion Models. CoRR abs/2210.02303 (2022) - [i21]Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei, Ying Nian Wu:
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells. CoRR abs/2210.02684 (2022) - [i20]Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans:
On Distillation of Guided Diffusion Models. CoRR abs/2210.03142 (2022) - 2021
- [b1]Ruiqi Gao:
Effective Learning of Descriptive and Generator Models and Learning Representations for Grid Cells and V1 Cells. University of California, Los Angeles, USA, 2021 - [c15]Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Ying Nian Wu:
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis. CVPR 2021: 9959-9968 - [c14]Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma:
Learning Energy-Based Models by Diffusion Recovery Likelihood. ICLR 2021 - [c13]Tianle Cai, Ruiqi Gao, Jason D. Lee, Qi Lei:
A Theory of Label Propagation for Subpopulation Shift. ICML 2021: 1170-1182 - [c12]Ruiqi Gao, Jianwen Xie, Xue-Xin Wei, Song-Chun Zhu, Ying Nian Wu:
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling. NeurIPS 2021: 28623-28635 - [i19]Tianle Cai, Ruiqi Gao, Jason D. Lee, Qi Lei:
A Theory of Label Propagation for Subpopulation Shift. CoRR abs/2102.11203 (2021) - [i18]Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Yingnian Wu:
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis. CoRR abs/2104.01508 (2021) - 2020
- [j2]Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu:
Cooperative Training of Descriptor and Generator Networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(1): 27-45 (2020) - [c11]Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns. AAAI 2020: 12442-12451 - [c10]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CVPR 2020: 7515-7525 - [c9]Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, Jason D. Lee:
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot. NeurIPS 2020 - [i17]Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC. CoRR abs/2006.06897 (2020) - [i16]Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
A Representational Model of Grid Cells Based on Matrix Lie Algebras. CoRR abs/2006.10259 (2020) - [i15]Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, Jason D. Lee:
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot. CoRR abs/2009.11094 (2020) - [i14]Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma:
Learning Energy-Based Models by Diffusion Recovery Likelihood. CoRR abs/2012.08125 (2020) - [i13]Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu:
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis. CoRR abs/2012.13522 (2020)
2010 – 2019
- 2019
- [c8]Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Learning Dynamic Generator Model by Alternating Back-Propagation through Time. AAAI 2019: 5498-5507 - [c7]Xianglei Xing, Tian Han, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu:
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network. CVPR 2019: 10354-10363 - [c6]Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion. ICLR (Poster) 2019 - [c5]Kun Han, Haiwei Pan, Ruiqi Gao, Jieyao Yu, Bin Yang:
Multimodal 3D Convolutional Neural Networks for Classification of Brain Disease Using Structural MR and FDG-PET Images. ICPCSEE (1) 2019: 658-668 - [c4]Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, Jason D. Lee:
Convergence of Adversarial Training in Overparametrized Neural Networks. NeurIPS 2019: 13009-13020 - [i12]Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1. CoRR abs/1902.03871 (2019) - [i11]Tianle Cai, Ruiqi Gao, Jikai Hou, Siyu Chen, Dong Wang, Di He, Zhihua Zhang, Liwei Wang:
A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems. CoRR abs/1905.11675 (2019) - [i10]Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang, Cho-Jui Hsieh, Jason D. Lee:
Convergence of Adversarial Training in Overparametrized Networks. CoRR abs/1906.07916 (2019) - [i9]Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns. CoRR abs/1911.11294 (2019) - [i8]Jianwen Xie, Ruiqi Gao, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Representation Learning: A Statistical Perspective. CoRR abs/1911.11374 (2019) - [i7]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CoRR abs/1912.00589 (2019) - 2018
- [c3]Jianwen Xie, Yang Lu, Ruiqi Gao, Ying Nian Wu:
Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching. AAAI 2018: 4292-4301 - [c2]Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu:
Learning Descriptor Networks for 3D Shape Synthesis and Analysis. CVPR 2018: 8629-8638 - [c1]Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu:
Learning Generative ConvNets via Multi-Grid Modeling and Sampling. CVPR 2018: 9155-9164 - [i6]Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu:
Learning Descriptor Networks for 3D Shape Synthesis and Analysis. CoRR abs/1804.00586 (2018) - [i5]Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu:
Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry. CoRR abs/1806.06298 (2018) - [i4]Ying Nian Wu, Ruiqi Gao, Tian Han, Song-Chun Zhu:
A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models. CoRR abs/1810.04261 (2018) - [i3]Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion. CoRR abs/1810.05597 (2018) - [i2]Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Learning Dynamic Generator Model by Alternating Back-Propagation Through Time. CoRR abs/1812.10587 (2018) - 2017
- [j1]Hong Song, Lei Chen, Ruiqi Gao, Iordachescu Ilie Mihaita Bogdan, Jian Yang, Shuliang Wang, Wentian Dong, Wenxiang Quan, Weimin Dang, Xin Yu:
Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM. BMC Medical Informatics Decis. Mak. 17(S-3): 166:1-166:9 (2017) - [i1]Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu:
Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence. CoRR abs/1709.08868 (2017)
Coauthor Index
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last updated on 2024-11-19 21:47 CET by the dblp team
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