RUIQI GAO
RUIQI GAO
Research Scientist, Google Brain
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Cooperative training of descriptor and generator networks
J Xie, Y Lu, R Gao, SC Zhu, YN Wu
IEEE transactions on pattern analysis and machine intelligence 42 (1), 27-45, 2018
902018
Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion
R Gao, J Xie, SC Zhu, YN Wu
arXiv preprint arXiv:1810.05597, 2018
88*2018
Learning Descriptor Networks for 3D Shape Synthesis and Analysis
J Xie, Z Zheng, R Gao, W Wang, SC Zhu, YN Wu
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
802018
Learning generative convnets via multi-grid modeling and sampling
R Gao, Y Lu, J Zhou, SC Zhu, YN Wu
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
482018
Cooperative learning of energy-based model and latent variable model via mcmc teaching
J Xie, Y Lu, R Gao, YN Wu
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
452018
Flow contrastive estimation of energy-based models
R Gao, E Nijkamp, DP Kingma, Z Xu, AM Dai, YN Wu
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
332020
Learning dynamic generator model by alternating back-propagation through time
J Xie, R Gao, Z Zheng, SC Zhu, YN Wu
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5498-5507, 2019
252019
Unsupervised disentangling of appearance and geometry by deformable generator network
X Xing, T Han, R Gao, SC Zhu, YN Wu
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
212019
Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry
X Xing, R Gao, T Han, SC Zhu, YN Wu
arXiv preprint arXiv:1806.06298, 2018
182018
Learning grid cells as vector representation of self-position coupled with matrix representation of self-motion
R Gao, J Xie, SC Zhu, YN Wu
arXiv preprint arXiv:1810.05597, 2018
152018
Learning Energy-Based Models by Diffusion Recovery Likelihood
R Gao, Y Song, B Poole, YN Wu, DP Kingma
arXiv preprint arXiv:2012.08125, 2020
132020
A remark on copy number variation detection methods
S Li, X Dou, R Gao, X Ge, M Qian, L Wan
PloS one 13 (4), e0196226, 2018
132018
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
J Xie, Z Zheng, R Gao, W Wang, SC Zhu, YN Wu
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
112020
A tale of three probabilistic families: Discriminative, descriptive, and generative models
YN Wu, R Gao, T Han, SC Zhu
Quarterly of Applied Mathematics 77 (2), 423-465, 2019
82019
Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC
E Nijkamp, R Gao, P Sountsov, S Vasudevan, B Pang, SC Zhu, YN Wu
arXiv preprint arXiv:2006.06897, 2020
72020
Motion-based generator model: Unsupervised disentanglement of appearance, trackable and intrackable motions in dynamic patterns
J Xie, R Gao, Z Zheng, SC Zhu, YN Wu
Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 12442 …, 2020
62020
Representation Learning: A Statistical Perspective
J Xie, R Gao, E Nijkamp, SC Zhu, YN Wu
Annual Review of Statistics and Its Application 7, 303-335, 2020
52020
Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1
R Gao, J Xie, SC Zhu, YN Wu
arXiv preprint arXiv:1902.03871, 2019
4*2019
Exploring generative perspective of convolutional neural networks by learning random field models
Y Lu, R Gao, SC Zhu, YN Wu
Statistics and Its Interface 11 (3), 515-529, 2018
22018
Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence
R Gao, Y Lu, J Zhou, SC Zhu, YN Wu
arXiv preprint arXiv:1709.08868, 2017
22017
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