| MetaGAN: An Adversarial Approach to Few-Shot Learning R Zhang, T Che, Z Ghahramani, Y Bengio, Y Song NeurIPS 2, 8, 2018 | 703 | 2018 |
| 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 | 325 | |
| 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 | 291 | 2017 |
| Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning T Chen, R Zhang, G Hinton arXiv preprint arXiv:2208.04202, 2022 | 267 | 2022 |
| Perceptual Generative Autoencoders Z Zhang, R Zhang, Z Li, Y Bengio, L Paull arXiv preprint arXiv:1906.10335, 2019 | 148* | 2019 |
| An Attention Free Transformer S Zhai, W Talbott, N Srivastava, C Huang, H Goh, R Zhang, J Susskind arXiv preprint arXiv:2105.14103, 2021 | 146 | 2021 |
| 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 | 137 | 2020 |
| 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 | 67 | 2021 |
| 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 | 12 | 2022 |
| How Far Are We from Intelligent Visual Deductive Reasoning? Y Zhang, H Bai, R Zhang, J Gu, S Zhai, J Susskind, N Jaitly arXiv preprint arXiv:2403.04732, 2024 | 11 | 2024 |
| Hierarchical prediction and adversarial learning for conditional response generation Y Li, R Zhang, W Li, Z Cao IEEE Transactions on Knowledge and Data Engineering 34 (1), 314-327, 2020 | 10 | 2020 |
| Improving GFlowNets for Text-to-Image Diffusion Alignment D Zhang, Y Zhang, J Gu, R Zhang, J Susskind, N Jaitly, S Zhai arXiv preprint arXiv:2406.00633, 2024 | 8 | 2024 |
| 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 | 8 | 2020 |
| dMel: Speech Tokenization made Simple H Bai, T Likhomanenko, R Zhang, Z Gu, Z Aldeneh, N Jaitly arXiv preprint arXiv:2407.15835, 2024 | 7 | 2024 |
| Learning Representation from Neural Fisher Kernel with Low-rank Approximation R Zhang, S Zhai, E Littwin, J Susskind arXiv preprint arXiv:2202.01944, 2022 | 5 | 2022 |
| Normalizing Flows are Capable Generative Models S Zhai, R Zhang, P Nakkiran, D Berthelot, J Gu, H Zheng, T Chen, ... arXiv preprint arXiv:2412.06329, 2024 | 2 | 2024 |
| TypeScore: A Text Fidelity Metric for Text-to-Image Generative Models GG Sampaio, R Zhang, S Zhai, J Gu, J Susskind, N Jaitly, Y Zhang arXiv preprint arXiv:2411.02437, 2024 | 1 | 2024 |
| Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo J Thornton, L Bethune, R Zhang, A Bradley, P Nakkiran, S Zhai arXiv preprint arXiv:2502.12786, 2025 | | 2025 |
| Controlled Generation with Distilled Diffusion Energy Models and Sequential Monte Carlo J Thornton, L Béthune, R ZHANG, A Bradley, P Nakkiran, S Zhai The 28th International Conference on Artificial Intelligence and Statistics, 0 | | |
| A Dot Product Attention Free Transformer S Zhai, W Talbott, N Srivastava, C Huang, H Goh, R ZHANG, JM Susskind | | |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARPreverjeni e-poštni naslov na umontreal.ca
