| Deep compressed sensing Y Wu, M Rosca, T Lillicrap International Conference on Machine Learning, 6850-6860, 2019 | 196 | 2019 |
| Optimizing agent behavior over long time scales by transporting value CC Hung, T Lillicrap, J Abramson, Y Wu, M Mirza, F Carnevale, A Ahuja, ... Nature communications 10 (1), 5223, 2019 | 143 | 2019 |
| Shaping belief states with generative environment models for rl K Gregor, D Jimenez Rezende, F Besse, Y Wu, H Merzic, A van den Oord Advances in Neural Information Processing Systems 32, 2019 | 121 | 2019 |
| Logan: Latent optimisation for generative adversarial networks Y Wu, J Donahue, D Balduzzi, K Simonyan, T Lillicrap arXiv preprint arXiv:1912.00953, 2019 | 115 | 2019 |
| Towards learning universal audio representations L Wang, P Luc, Y Wu, A Recasens, L Smaira, A Brock, A Jaegle, ... ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 76 | 2022 |
| The kanerva machine: A generative distributed memory Y Wu, G Wayne, A Graves, T Lillicrap arXiv preprint arXiv:1804.01756, 2018 | 51 | 2018 |
| Training generative adversarial networks by solving ordinary differential equations C Qin, Y Wu, JT Springenberg, A Brock, J Donahue, T Lillicrap, P Kohli Advances in Neural Information Processing Systems 33, 5599-5609, 2020 | 40 | 2020 |
| Learning attractor dynamics for generative memory Y Wu, G Wayne, K Gregor, T Lillicrap Advances in Neural Information Processing Systems 31, 2018 | 22 | 2018 |
| Discretization drift in two-player games MC Rosca, Y Wu, B Dherin, D Barrett International Conference on Machine Learning, 9064-9074, 2021 | 13 | 2021 |
| Product kanerva machines: Factorized bayesian memory A Marblestone, Y Wu, G Wayne arXiv preprint arXiv:2002.02385, 2020 | 7 | 2020 |
| Deep compressed sensing. arXiv 2019 Y Wu, M Rosca, T Lillicrap arXiv preprint arXiv:1905.06723, 0 | 7 | |
| On a continuous time model of gradient descent dynamics and instability in deep learning M Rosca, Y Wu, C Qin, B Dherin arXiv preprint arXiv:2302.01952, 2023 | 6 | 2023 |
| Compressed sensing using neural networks Y Wu, TP Lillicrap, M Rosca US Patent 12,032,523, 2024 | 5 | 2024 |
| A simulation study of deep belief network combined with the self-organizing mechanism of adaptive resonance theory Y Wu, HJ Cai 2010 International Conference on Computational Intelligence and Software …, 2010 | 5 | 2010 |
| Predicting generalization with degrees of freedom in neural networks E Grant, Y Wu ICML 2022 2nd AI for Science Workshop, 2022 | 4 | 2022 |
| Kanerva++: extending the Kanerva machine with differentiable, locally block allocated latent memory J Ramapuram, Y Wu, A Kalousis arXiv preprint arXiv:2103.03905, 2021 | 4 | 2021 |
| In Search of Lost Time Y Wu University of Waterloo, 2012 | 3 | 2012 |
| Finding a stationary point of a loss function by an iterative algorithm using a variable learning rate value M Rosca, BRU Dherin, Y Wu, C Qin US Patent App. 18/232,291, 2024 | | 2024 |
| Stable and efficient training of adversarial models by an iterated update operation of second order or higher C Qin, Y Wu US Patent App. 17/337,376, 2021 | | 2021 |
| Mixed Selectivity via Unsupervised Learning in Neural Networks Y Wu | | 2020 |
Timothy P. LillicrapDirector of Research, Google DeepMindVerified email at google.com
