In this paper, we provide a rigorous proof of convergence of the Adaptive Moment Estimate
(Adam) algorithm for a wide class of optimization objectives. Despite the popularity and …

Adam and RMSProp are two of the most influential adaptive stochastic algorithms for
training deep neural networks, which have been pointed out to be divergent even in the …

Ever since\citet {reddi2019convergence} pointed out the divergence issue of Adam, many
new variants have been designed to obtain convergence. However, vanilla Adam remains …

While stochastic gradient descent (SGD) is still the de facto algorithm in deep learning,
adaptive methods like Clipped SGD/Adam have been observed to outperform SGD across …

Adaptive learning is a personalized instruction system that adjusts to the needs,
preferences, and progress of learners. This paper reviews the current and future …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

Adam is widely adopted in practical applications due to its fast convergence. However, its
theoretical analysis is still far from satisfactory. Existing convergence analyses for Adam rely …

Abstract Recently, Arjevani et al.[1] establish a lower bound of iteration complexity for the
first-order optimization under an $ L $-smooth condition and a bounded noise variance …

Accurate power time-series prediction is an important application for building new
industrialized smart cities. The gated recurrent units (GRUs) models have been successfully …

While stochastic gradient descent (SGD) is still the de facto algorithm in deep learning,
adaptive methods like Adam have been observed to outperform SGD across important tasks …