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Fabio Pardo
Fabio Pardo
Google DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
4192023
Action branching architectures for deep reinforcement learning
A Tavakoli, F Pardo, P Kormushev
AAAI Conference on Artificial Intelligence, 2018
2752018
Time limits in reinforcement learning
F Pardo, A Tavakoli, V Levdik, P Kormushev
International Conference on Machine Learning, 2018
1572018
CoMic: Complementary Task Learning & Mimicry for Reusable Skills
L Hasenclever, F Pardo, R Hadsell, N Heess, J Merel
International Conference on Machine Learning, 2020
452020
Tonic: A deep reinforcement learning library for fast prototyping and benchmarking
F Pardo
arXiv preprint arXiv:2011.07537, 2020
312020
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
F Pardo, V Levdik, P Kormushev
AAAI Conference on Artificial Intelligence, 2020
14*2020
Ostrichrl: A musculoskeletal ostrich simulation to study bio-mechanical locomotion
V La Barbera, F Pardo, Y Tassa, M Daley, C Richards, P Kormushev, ...
arXiv preprint arXiv:2112.06061, 2021
122021
Ivy: Templated deep learning for inter-framework portability
D Lenton, F Pardo, F Falck, S James, R Clark
arXiv preprint arXiv:2102.02886, 2021
72021
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
62024
Vision-language models as a source of rewards
K Baumli, S Baveja, F Behbahani, H Chan, G Comanici, S Flennerhag, ...
arXiv preprint arXiv:2312.09187, 2023
12023
Design and training of deep reinforcement learning agents
F Pardo
Imperial College London, 2022
2022
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Articles 1–11