5.7k citations · 15.5k across the 75 of their papers we have counts for
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On Warm-Starting Neural Network Training
Jordan T. Ash, Ryan P. Adams
In many real-world deployments of machine learning systems, data arrive piecemeal. These learning scenarios may be passive, where data arrive incrementally due to structural proper…
Discrete Object Generation with Reversible Inductive Construction
Ari Seff, Wenda Zhou, Farhan Damani +2
The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs. However, constructio…
A Theoretical Connection Between Statistical Physics and Reinforcement Learning
Jad Rahme, Ryan P. Adams
Sequential decision making in the presence of uncertainty and stochastic dynamics gives rise to distributions over state/action trajectories in reinforcement learning (RL) and opti…
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
Igor Fedorov, Ryan P. Adams, Matthew Mattina +1
The vast majority of processors in the world are actually microcontroller units (MCUs), which find widespread use performing simple control tasks in applications ranging from autom…
Efficient Optimization of Loops and Limits with Randomized Telescoping Sums
Alex Beatson, Ryan P. Adams
We consider optimization problems in which the objective requires an inner loop with many steps or is the limit of a sequence of increasingly costly approximations. Meta-learning,…