Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ... nature 575 (7782), 350-354, 2019 | 4920 | 2019 |
Population based training of neural networks M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ... arXiv preprint arXiv:1711.09846, 2017 | 909 | 2017 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 703 | 2024 |
Alphastar: Mastering the real-time strategy game starcraft ii O Vinyals, I Babuschkin, J Chung, M Mathieu, M Jaderberg, ... DeepMind blog 2, 20, 2019 | 571 | 2019 |
Open-ended learning leads to generally capable agents OEL Team, A Stooke, A Mahajan, C Barros, C Deck, J Bauer, J Sygnowski, ... arXiv preprint arXiv:2107.12808, 2021 | 167 | 2021 |
BOAT: Building auto-tuners with structured Bayesian optimization V Dalibard, M Schaarschmidt, E Yoneki Proceedings of the 26th International Conference on World Wide Web, 479-488, 2017 | 108 | 2017 |
Robocat: A self-improving foundation agent for robotic manipulation K Bousmalis, G Vezzani, D Rao, C Devin, AX Lee, M Bauza, T Davchev, ... arXiv preprint arXiv:2306.11706, 2023 | 86 | 2023 |
A generalized framework for population based training A Li, O Spyra, S Perel, V Dalibard, M Jaderberg, C Gu, D Budden, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 78 | 2019 |
Rapid training of deep neural networks without skip connections or normalization layers using deep kernel shaping J Martens, A Ballard, G Desjardins, G Swirszcz, V Dalibard, ... arXiv preprint arXiv:2110.01765, 2021 | 53 | 2021 |
Discovering evolution strategies via meta-black-box optimization R Lange, T Schaul, Y Chen, T Zahavy, V Dalibard, C Lu, S Singh, ... Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023 | 47 | 2023 |
Population Based Training of Neural Networks (PBT) M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ... | 46* | 2017 |
PrefEdge: SSD prefetcher for large-scale graph traversal K Nilakant, V Dalibard, A Roy, E Yoneki Proceedings of International Conference on Systems and Storage, 1-12, 2014 | 39 | 2014 |
Discovering attention-based genetic algorithms via meta-black-box optimization R Lange, T Schaul, Y Chen, C Lu, T Zahavy, V Dalibard, S Flennerhag Proceedings of the Genetic and Evolutionary Computation Conference, 929-937, 2023 | 31 | 2023 |
Robocat: A self-improving generalist agent for robotic manipulation K Bousmalis, G Vezzani, D Rao, CM Devin, AX Lee, MB Villalonga, ... Transactions on Machine Learning Research, 2023 | 18 | 2023 |
Faster improvement rate population based training V Dalibard, M Jaderberg arXiv preprint arXiv:2109.13800, 2021 | 10 | 2021 |
Learning runtime parameters in computer systems with delayed experience injection M Schaarschmidt, F Gessert, V Dalibard, E Yoneki arXiv preprint arXiv:1610.09903, 2016 | 10 | 2016 |
Population based training of neural networks ME Jaderberg, W Czarnecki, TFG Green, VC Dalibard US Patent 11,604,985, 2023 | 8 | 2023 |
A framework to build bespoke auto-tuners with structured Bayesian optimisation V Dalibard University of Cambridge, Computer Laboratory, 2017 | 8 | 2017 |
Perception-prediction-reaction agents for deep reinforcement learning A Stooke, V Dalibard, SM Jayakumar, WM Czarnecki, M Jaderberg arXiv preprint arXiv:2006.15223, 2020 | 2 | 2020 |
Tuning the scheduling of distributed stochastic gradient descent with Bayesian optimization V Dalibard, M Schaarschmidt, E Yoneki arXiv preprint arXiv:1612.00383, 2016 | 2 | 2016 |