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Ruixiang Zhang
Ruixiang Zhang
Mila, Université de Montréal
Verified email at mila.quebec - Homepage
Title
Cited by
Cited by
Year
MetaGAN: An Adversarial Approach to Few-Shot Learning
R Zhang, T Che, Z Ghahramani, Y Bengio, Y Song
NeurIPS 2, 8, 2018
3402018
Maximum-likelihood augmented discrete generative adversarial networks
T Che, Y Li, R Zhang, RD Hjelm, W Li, Y Song, Y Bengio
arXiv preprint arXiv:1702.07983, 2017
2502017
Understanding hidden memories of recurrent neural networks
Y Ming, S Cao, R Zhang, Z Li, Y Chen, Y Song, H Qu
2017 IEEE Conference on Visual Analytics Science and Technology (VAST), 13-24, 2017
1852017
Perceptual Generative Autoencoders
Z Zhang, R Zhang, Z Li, Y Bengio, L Paull
arXiv preprint arXiv:1906.10335, 2019
92*2019
Your GAN is secretly an energy-based model and you should use discriminator driven latent sampling
T Che, R Zhang, J Sohl-Dickstein, H Larochelle, L Paull, Y Cao, Y Bengio
Advances in Neural Information Processing Systems 33, 12275-12287, 2020
692020
Deep verifier networks: Verification of deep discriminative models with deep generative models
T Che, X Liu, S Li, Y Ge, R Zhang, C Xiong, Y Bengio
Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7002-7010, 2021
522021
An Attention Free Transformer
S Zhai, W Talbott, N Srivastava, C Huang, H Goh, R Zhang, J Susskind
arXiv preprint arXiv:2105.14103, 2021
202021
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
T Chen, R Zhang, G Hinton
arXiv preprint arXiv:2208.04202, 2022
132022
Maximum-likelihood augmented discrete generative adversarial networks. arXiv 2017
T Che, Y Li, R Zhang, RD Hjelm, W Li, Y Song, Y Bengio
arXiv preprint arXiv:1702.07983, 0
6
Maximum-likelihood augmented discrete generative adversarial networks (2017)
T Che, Y Li, R Zhang, RD Hjelm, W Li, Y Song, Y Bengio
arXiv preprint arXiv:1702.07983, 0
5
Hierarchical Prediction and Adversarial Learning For Conditional Response Generation
Y Li, R Zhang, W Li, Z Cao
IEEE Transactions on Knowledge and Data Engineering, 2020
42020
Learning Structured Latent Factors from Dependent Data: A Generative Model Framework from Information-Theoretic Perspective
R Zhang, M Koyama, K Ishiguro
International Conference on Machine Learning, 11141-11152, 2020
22020
Learning Representation from Neural Fisher Kernel with Low-rank Approximation
R Zhang, S Zhai, E Littwin, J Susskind
arXiv preprint arXiv:2202.01944, 2022
12022
Robust and Controllable Object-Centric Learning through Energy-based Models
R Zhang, T Che, B Ivanovic, R Wang, M Pavone, Y Bengio, L Paull
arXiv preprint arXiv:2210.05519, 2022
2022
A Dot Product Attention Free Transformer
S Zhai, W Talbott, N Srivastava, C Huang, H Goh, R ZHANG, JM Susskind
2021
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