What is AdaSmooth?
AdaSmooth is a stochastic optimization technique used to improve the learning rate method for stochastic gradient descent (SGD) algorithms. It is an extension of the Adagrad and AdaDelta optimization methods that aim to reduce the aggressive, monotonically decreasing learning rate. AdaSmooth uses per-dimension learning rate, which makes it faster and less sensitive to hyperparameters.
How does AdaSmooth work?
AdaSmooth adaptively selects the size of the window instead of accumulating all past squared gradients. Given the window size M, AdaSmooth calculates the effective ratio by:
et = |(xt - xt-M)| / nt
where nt = ∑i=0M-1|(xt-i - xt-1-i)|
Given the effective ratio, the scaled smoothing constant is obtained by:
ct = ( ρ2- ρ1) × et + (1- ρ2)
where ρ1 is approximately 0.5 and ρ2 is approximately 0.99. The running average E[g2]t at time step t depends only on the previous average and current gradient and is calculated by:
E[g2]t = c2∘gt2 + (1 - c2)∘E[g2]t-1
The update step is then:
Δxt = -η / (√(E[g2]t + ε)) ∘ gt
which is incorporated into the final update:
xt+1 = xt + Δxt
This method leads to a faster convergence rate, making it more efficient compared to conventional gradient descent methods.
Advantages of AdaSmooth
The primary advantage of AdaSmooth is its adaptation to per-dimension learning rate, which optimizes the model in a faster and less sensitive way to hyperparameters. AdaSmooth also allows the optimizer to converge faster than other gradient descent optimization methods. In addition, AdaSmooth is effective in handling non-stationary data and provides good results for deep learning models.
In Conclusion
AdaSmooth is a powerful optimization method for stochastic gradient descent algorithms. AdaSmooth is an extension of Adagrad and AdaDelta and improves the learning rate process by utilizing per-dimension learning rate. This method provides a faster convergence rate and is less sensitive to hyperparameters. AdaSmooth is a useful technique in deep learning models for handling non-stationary data and overall optimization.
