198 citations · 1.1k across the 35 of their papers we have counts for
5 papers · 1 filter
Differentiable Convex Optimization Layers
Akshay Agrawal, Brandon Amos, Shane Barratt +3
Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectur…
Generalized Inner Loop Meta-Learning
Edward Grefenstette, Brandon Amos, Denis Yarats +6
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimiza…
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
Denis Yarats, Amy Zhang, Ilya Kostrikov +3
Training an agent to solve control tasks directly from high-dimensional images with model-free reinforcement learning (RL) has proven difficult. A promising approach is to learn a…
The Differentiable Cross-Entropy Method
Brandon Amos, Denis Yarats
We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us…
The Limited Multi-Label Projection Layer
Brandon Amos, Vladlen Koltun, J. Zico Kolter
We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi…