DEAM: Accumulated Momentum with Discriminative Weight for Stochastic Optimization

Optimization algorithms with momentum, e.g., Nesterov Accelerated Gradient and ADAM, have been widely used for building deep learning models because of their faster convergence rates compared to stochastic gradient descent (SGD). Momentum is a method that helps accelerate SGD in the relevant directions in variable updating, which can minify the oscillations of variables update route. Optimization algorithms with momentum usually allocate a fixed hyperparameter (e.g., \beta_1) as the weight of the momentum term. However, using a fixed weight is not applicable to some situations, and such a hyper-parameter can be extremely hard to tune in applications. In this paper, we will introduce a new optimization algorithm, namely DEAM (Discriminative wEight on Accumulated Momentum). Instead of assigning the momentum term with a fixed weight, DEAM proposes to compute the momentum weight in the learning process automatically. DEAM also involves a "backtrack" term, which can help accelerate the algorithm convergence by restricting redundant updates. Extensive experiments have been done on several real-world datasets. The experimental results demonstrate that DEAM can achieve a faster convergence rate than the existing optimization algorithms in training both the classic machine learning models and the recent deep learning models.