3 citations · 3 across the 12 of their papers we have counts for
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Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning
Edwin De Nicolo, Rahul Marchand, Cornelius Carlsson +2
Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defin…
A Physics-Inspired Optimizer: Velocity Regularized Adam
Pranav Vaidhyanathan, Lucas Schorling, Natalia Ares +1
We introduce Velocity-Regularized Adam (VRAdam), a physics-inspired optimizer for training deep neural networks that draws on ideas from quartic terms for kinetic energy with its s…
MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence
Pranav Vaidhyanathan, Aristotelis Papatheodorou, Mark T. Mitchison +2
Scalable and generalizable physics-aware deep learning has long been considered a significant challenge with various applications across diverse domains ranging from robotics to mo…