ADASHIFT: DECORRELATION AND CONVERGENCE OF ADAPTIVE LEARNING RATE METHODS
Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of nonconvergence of Adam, but their efficiency turns out to be unsatisfactory in practice. In this paper, we provide a new insight into the non-convergence issue of Adam as well as other adaptive learning rate methods. We argue that there exists an inappropriate correlation between gradient g t and the second moment term v t in Adam (t is the timestep), which results in that a large gradient is likely to have small step size while a small gradient may have a large step size. We demonstrate that such unbalanced step sizes are the fundamental cause of non-convergence of Adam, and we further prove that decorrelating v t and g t will lead to unbiased step size for each gradient, thus solving the non-convergence problem of Adam. Finally, we propose AdaShift, a novel adaptive learning rate method that decorrelates v t and g t by temporal shifting, i.e., using temporally shifted gradient g t−n to calculate v t . The experiment results demonstrate that AdaShift is able to address the non-convergence issue of Adam, while still maintaining a competitive performance with Adam in terms of both training speed and generalization.
This manuscript contributes a new online gradient descent algorithm with adaptation to local curvature, in the style of the Adam optimizer, ie with a diagonal reweighting of the gradient that serves as an adaptive step size. First the authors identify a limitation of Adam: the adaptive step size decreases with the gradient magnitude. The paper is well written.
The strengths of the paper are a interesting theoretical analysis of convergence difficulties in ADAM, a proposal for an improvement, and nice empirical results that shows good benefits. In my eyes, the limitations of the paper are that the example studied is a bit contrived and as a results, I am not sure how general the improvements.
# Specific comments and suggestions
Under the ambitious term "theorem", the results of theorem 2 and 3 limited to the example of failure given in eq 6. I would have been more humble, and called such analyses "lemma". Similarly, theorem 4 is an extension of this example to stochastic online settings. More generally, I am worried that the theoretical results and the intuitions backing the improvements are built only on one pathological example. Are there arguments to claim that this example is a prototype for a more general behavior?
Ali Rahimi presented a very simple example of poor perform of the Adam optimizer in his test-of-time award speech at NIPS this year (https://www.youtube.com/watch?v=Qi1Yry33TQE): a very ill-conditioned factorized linear model (product of two matrices that correspond to two different layers) with a square loss. It seems like an excellent test for any optimizer that tries to be robust to ill-conditioning (as with Adam), though I suspect that the problem solved here is a different one than the problem raised by Rahimi's example.
With regards to the solution proposed, temporal decorrelation, I wonder how it interacts with mini-batch side. With only a light understanding of the problem, it seems to me that large mini-batches will decrease the variance of the gradient estimates and hence increase the correlation of successive samples, breaking the assumptions of the method.
Using a shared scalar across the multiple dimensions implies that the direction of the step is now the same as that of the gradient. This is a strong departure compared to ADAM. It would be interesting to illustrate the two behaviors to optimize an ill-conditioned quadratic function, for which the gradient direction is not a very good choice.
The performance gain compared to ADAM seems consistent. It would have been interesting to see Nadam in the comparisons.
I would like to congratulate the authors for sharing code.
There is a typo on the y label of figure 4 right.