Papers Read on AI

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November 1, 2022  

Amos: An Adam-style Optimizer with Adaptive Weight Decay towards Model-Oriented Scale

November 1, 2022

We present Amos , a stochastic gradient-based optimizer designed for training deep neural networks. It can be viewed as an Adam optimizer with theoretically supported, adaptive learning-rate decay and weight decay. A key insight behind Amos is that it leverages model-specific information to determine the initial learning-rate and decaying schedules. When used for pre-training BERT variants and T5, Amos consistently converges faster than the state-of-the-art settings of AdamW, achieving better validation loss within ≤ 70% training steps and time, while requiring ≤ 51% memory for slot variables. Our code is open-sourced at: https: //github.com/google-research/jestimator .

2022: Ran Tian, Ankur P. Parikh

https://arxiv.org/pdf/2210.11693v1.pdf