In the coming 6G communications, network densification, high throughput, positioning
accuracy, energy efficiency, and many other key performance indicator requirements are …

The question answering system is frequently applied in the area of natural language
processing (NLP) because of the wide variety of applications. It consists of answering …

Meta-learning has been proposed as a framework to address the challenging few-shot
learning setting. The key idea is to leverage a large number of similar few-shot tasks in order …

Transfer learning (TL) has been successfully applied to many real-world problems that
traditional machine learning (ML) cannot handle, such as image processing, speech …

Unsupervised domain adaptation (UDA) aims to transfer the knowledge learnt from a
labeled source domain to an unlabeled target domain. Previous work is mainly built upon …

We introduce a new measure to evaluate the transferability of representations learned by
classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy …

Spatial-temporal prediction is a fundamental problem for constructing smart city, which is
useful for tasks such as traffic control, taxi dispatching, and environment policy making. Due …

We propose a novel approach for estimating the difficulty and transferability of supervised
classification tasks. Unlike previous work, our approach is solution agnostic and does not …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Coffee is an important export product of the tropical countries where it is grown. Therefore,
the separation of coffee beans in the world in terms of the quality element and variety forgery …