| Demystifying MMD GANs M Bińkowski, DJ Sutherland, M Arbel, A Gretton 6th International Conference on Learning Representations, 2018 | 860 | 2018 |
| Flamingo: a visual language model for few-shot learning JB Alayrac, J Donahue, P Luc, A Miech, I Barr, Y Hasson, K Lenc, ... Advances in Neural Information Processing Systems 35, 23716-23736, 2022 | 481 | 2022 |
| High fidelity speech synthesis with adversarial networks M Bińkowski, J Donahue, S Dieleman, A Clark, E Elsen, N Casagrande, ... 8th International Conference on Learning Representations, 2019 | 231 | 2019 |
| Autoregressive convolutional neural networks for asynchronous time series M Bińkowski, G Marti, P Donnat Proceedings of the 35th International Conference on Machine Learning 80, 580-589, 2017 | 158 | 2017 |
| End-to-end adversarial text-to-speech J Donahue, S Dieleman, M Bińkowski, E Elsen, K Simonyan arXiv preprint arXiv:2006.03575, 2020 | 151 | 2020 |
| A review of two decades of correlations, hierarchies, networks and clustering in financial markets G Marti, F Nielsen, M Bińkowski, P Donnat Progress in information geometry: Theory and applications, 245-274, 2021 | 113 | 2021 |
| On gradient regularizers for MMD GANs M Arbel, DJ Sutherland, M Bińkowski, A Gretton Advances in neural information processing systems 31, 2018 | 94 | 2018 |
| Step-unrolled denoising autoencoders for text generation N Savinov, J Chung, M Binkowski, E Elsen, A Oord arXiv preprint arXiv:2112.06749, 2021 | 36 | 2021 |
| Batch Weight for Domain Adaptation With Mass Shift M Bińkowski, RD Hjelm, A Courville The IEEE International Conference on Computer Vision (ICCV), 1844-1853, 2019 | 12 | 2019 |
| Flamingo: a visual language model for few-shot learning, 2022 JB Alayrac, J Donahue, P Luc, A Miech, I Barr, Y Hasson, K Lenc, ... URL https://arxiv. org/abs/2204.14198, 0 | 12 | |
| Flamingo: A visual language model for few-shot learning. arXiv 2022 JB Alayrac, J Donahue, P Luc, A Miech, I Barr, Y Hasson, K Lenc, ... arXiv preprint arXiv:2204.14198, 0 | 6 | |
| Flamingo: a visual language model for few-shot learning. CoRR, abs/2204.14198, 2022. doi: 10.48550 JB Alayrac, J Donahue, P Luc, A Miech, I Barr, Y Hasson, K Lenc, ... arXiv preprint arXiv.2204.14198, 0 | 5 | |
| Endogeneous dynamics of intraday liquidity M Bińkowski, CA Lehalle arXiv preprint arXiv:1811.03766, 2018 | 4* | 2018 |
| A Deep Learning Approach for Characterizing Major Galaxy Mergers S Koppula, V Bapst, M Huertas-Company, S Blackwell, ... arXiv preprint arXiv:2102.05182, 2021 | 3 | 2021 |
| High fidelity speech synthesis with adversarial networks M Binkowski, K Simonyan, J Donahue, A Clark, SEL Dieleman, EK Elsen, ... US Patent App. 17/032,578, 2021 | 2 | 2021 |
| Adversarial Text-to-Speech for low-resource languages A Elneima, M Bińkowski Proceedings of the The Seventh Arabic Natural Language Processing Workshop …, 2022 | 1 | 2022 |
| Unsupervised one-to-many image translation S Lavoie-Marchildon, S Lachapelle, M Bińkowski, A Courville, Y Bengio, ... | 1 | 2018 |
| Generating audio data using unaligned text inputs with an adversarial network J Donahue, K Simonyan, SEL Dieleman, M Binkowski, EK Elsen US Patent App. 17/339,834, 2021 | | 2021 |
| Training and evaluating adversarial networks: from kernel discrepancies to applications M Binkowski Imperial College London, 2020 | | 2020 |
| Jean-Baptiste Alayrac Jeff Donahue Pauline Luc Antoine Miech I Barr, Y Hasson, K Lenc, A Mensch, K Millican, M Reynolds, R Ring, ... | | |
Sander DielemanResearch Scientist, DeepMindПотвърден имейл адрес: google.com
