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Valentin Dalibard
Valentin Dalibard
Verified email at cl.cam.ac.uk - Homepage
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Cited by
Year
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
21142019
Population based training of neural networks
M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ...
arXiv preprint arXiv:1711.09846, 2017
5352017
Alphastar: Mastering the real-time strategy game starcraft ii
O Vinyals, I Babuschkin, J Chung, M Mathieu, M Jaderberg, ...
DeepMind blog 2, 2019
4252019
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
872017
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
512021
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
402019
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
342014
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
132021
Population Based Training of Neural Networks (PBT)
M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ...
10*2017
Learning runtime parameters in computer systems with delayed experience injection
M Schaarschmidt, F Gessert, V Dalibard, E Yoneki
arXiv preprint arXiv:1610.09903, 2016
82016
A framework to build bespoke auto-tuners with structured Bayesian optimisation
V Dalibard
University of Cambridge, Computer Laboratory, 2017
72017
Faster improvement rate population based training
V Dalibard, M Jaderberg
arXiv preprint arXiv:2109.13800, 2021
32021
Tuning the scheduling of distributed stochastic gradient descent with Bayesian optimization
V Dalibard, M Schaarschmidt, E Yoneki
arXiv preprint arXiv:1612.00383, 2016
22016
Mitigating I/O latency in SSD-based graph traversal
A Roy, K Nilakant, V Dalibard, E Yoneki
University of Cambridge, Computer Laboratory, 2012
22012
Perception-prediction-reaction agents for deep reinforcement learning
A Stooke, V Dalibard, SM Jayakumar, WM Czarnecki, M Jaderberg
arXiv preprint arXiv:2006.15223, 2020
12020
Population Based Training as a Service
A Li, O Spyra, S Perel, V Dalibard, M Jaderberg, C Gu, D Budden, ...
NIPS Systems for ML Workshop, Montrťal, Canada, 2018
12018
Community detection in multi-layer networks
V Dalibard
Master’s thesis, University of Cambridge, 2012
12012
Population-based training of machine learning models
A Li, VC Dalibard, D Budden, O Spyra, ME Jaderberg, TJA Harley, S Perel, ...
US Patent App. 16/586,236, 2021
2021
Population based training of neural networks
ME Jaderberg, W Czarnecki, TFG Green, VC Dalibard
US Patent App. 16/766,631, 2021
2021
Open Source Project Running BSP on Ciel
V Dalibard
2012
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