1,108 research outputs found
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
We formulate the problem of neural network optimization as Bayesian
filtering, where the observations are the backpropagated gradients. While
neural network optimization has previously been studied using natural gradient
methods which are closely related to Bayesian inference, they were unable to
recover standard optimizers such as Adam and RMSprop with a root-mean-square
gradient normalizer, instead getting a mean-square normalizer. To recover the
root-mean-square normalizer, we find it necessary to account for the temporal
dynamics of all the other parameters as they are geing optimized. The resulting
optimizer, AdaBayes, adaptively transitions between SGD-like and Adam-like
behaviour, automatically recovers AdamW, a state of the art variant of Adam
with decoupled weight decay, and has generalisation performance competitive
with SGD
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