55 citations · 113 across the 25 of their papers we have counts for
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cs.LG2022★ 1 cited
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
Setareh Cohan, Nam Hee Kim, David Rolnick +1
Policies produced by deep reinforcement learning are typically characterised by their learning curves, but they remain poorly understood in many other respects. ReLU-based policies…
cs.LG2022★ 5 cited
TIML: Task-Informed Meta-Learning for Agriculture
Gabriel Tseng, Hannah Kerner, David Rolnick
Labeled datasets for agriculture are extremely spatially imbalanced. When developing algorithms for data-sparse regions, a natural approach is to use transfer learning from data-ri…