Deep neural network has been extensively applied to hyperspectral image (HSI)
classification recently. However, its success is greatly attributed to numerous labeled …

In supervised learning, acquiring labeled training data for a predictive model can be very
costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is …

Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

Active learning (AL) methods create an optimized labeled training set from unlabeled data.
We introduce a novel online active deep learning method for medical image analysis. We …

Regression problems are pervasive in real-world applications. Generally a substantial
amount of labeled samples are needed to build a regression model with good …

We present an Active Learning (AL) strategy for reusing a deep Convolutional Neural
Network (CNN)-based object detector on a new data set. This is of particular interest for …

Active learning (AL) is a machine-learning approach for reducing the data labeling effort.
Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a …

Active learning (AL) is a subfield of machine learning (ML) in which a learning algorithm
aims to achieve good accuracy with fewer training samples by interactively querying the …

Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …

Achieving faster execution with shorter compilation time can foster further diversity and
innovation in neural networks. However, the current paradigm of executing neural networks …