Jesse Farebrother
Jesse Farebrother
Mila / McGill University; Google DeepMind
Verified email at - Homepage
Cited by
Cited by
Generalization and Regularization in DQN
J Farebrother, MC Machado, M Bowling
NeurIPS Workshop on Deep Reinforcement Learning, 2018
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning
AA Taiga, R Agarwal, J Farebrother, A Courville, MG Bellemare
International Conference on Learning Representations (ICLR), 2023
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
J Farebrother*, J Greaves*, R Agarwal, C Le Lan, R Goroshin, PS Castro, ...
International Conference on Learning Representations (ICLR), 2023
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
CL Lan, J Greaves, J Farebrother, M Rowland, F Pedregosa, R Agarwal, ...
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
M Schwarzer*, J Farebrother*, J Greaves, K Roccapriore, E Cubuk, ...
AI for Accelerated Materials Design-NeurIPS 2023 Workshop, 2023
Using biconnected components for efficient identification of upstream features in large spatial networks (GIS cup)
Z Goldthorpe, J Cannon, J Farebrother, Z Friggstad, MA Nascimento
Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances …, 2018
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Articles 1–6