AERO's experimental optimizer is momentum SGD with Gaussian gradient noise, and its claimed state-of-the-art gains lack any baseline comparison.
Model-agnostic meta-learning for fast adap- tation of deep networks
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AERO: A Redirection-Based Optimization Framework Inspired by Judo for Robust Probabilistic Forecasting
AERO's experimental optimizer is momentum SGD with Gaussian gradient noise, and its claimed state-of-the-art gains lack any baseline comparison.