In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …

Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of
Transformer-based neural language models specialized for dialog, which have up to 137B …

The current expansion of theory and research on artificial intelligence in management and
organization studies has revitalized the theory and research on decision-making in …

The ability of learning from noisy labels is very useful in many visual recognition tasks, as a
vast amount of data with noisy labels are relatively easy to obtain. Traditionally, label noise …

Overfitting is one of the most critical challenges in deep neural networks, and there are
various types of regularization methods to improve generalization performance. Injecting …

The compression of Generative Adversarial Networks (GANs) has lately drawn attention,
due to the increasing demand for deploying GANs into mobile devices for numerous …

The comparative losses (typically, triplet loss) are appealing choices for learning person re-
identification (ReID) features. However, the triplet loss is computationally much more …

Generative adversarial networks (GANs) have gained increasing popularity in various
computer vision applications, and recently start to be deployed to resource-constrained …

Dropout is a mechanism to prevent deep neural networks from overfitting and improving
their generalization. Random dropout is the simplest method, where nodes are randomly …