On the Variance of the Adaptive Learning Rate and Beyond
The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate -- its variance is problematically large in the early stage, and presume warmup works as a variance reduction technique. We provide both empirical and theoretical evidence to verify our hypothesis. We further propose Rectified Adam (RAdam), a novel variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the efficacy and robustness of RAdam.
Authors propose a way to rectify the variance of the adaptive learning rate (RAdam) and apply the optimizer to applications in image classification, language modeling and neural machine translation. The experiments demonstrate not only a strong results over baseline Adam with warmup learning rate but the robustness of the optimizer. The authors additionally demonstrate the theoretical justification behind their optimizer, however I am not very qualified to make the judgement on the theory. Overall judging from the authors description of approach and experimental results, I recommend acceptance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
On the Variance of the Adaptive Learning Rate and Beyond
The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate -- its variance is problematically large in the early stage, and presume warmup works as a variance reduction technique. We provide both empirical and theoretical evidence to verify our hypothesis. We further propose Rectified Adam (RAdam), a novel variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the efficacy and robustness of RAdam.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



Authors propose a way to rectify the variance of the adaptive learning rate (RAdam) and apply the optimizer to applications in image classification, language modeling and neural machine translation. The experiments demonstrate not only a strong results over baseline Adam with warmup learning rate but the robustness of the optimizer. The authors additionally demonstrate the theoretical justification behind their optimizer, however I am not very qualified to make the judgement on the theory. Overall judging from the authors description of approach and experimental results, I recommend acceptance.
assistant
Authors propose a way to rectify the variance of the adaptive learning rate (RAdam) and apply the optimizer to applications in image classification, language modeling and neural machine translation. The experiments demonstrate not only a strong results over baseline Adam with warmup learning rate but the robustness of the optimizer. The authors additionally demonstrate the theoretical justification behind their optimizer, however I am not very qualified to make the judgement on the theory. Overall judging from the authors description of approach and experimental results, I recommend acceptance.