Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

As one of the important research topics in machine learning, loss function plays an important
role in the construction of machine learning algorithms and the improvement of their …

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning
technique for dimension reduction. UMAP is constructed from a theoretical framework based …

Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Person re-identification (re-ID) has become increasingly popular in the community due to its
application and research significance. It aims at spotting a person of interest in other …

The loss function, also known as cost function, is used for training a neural network or other
machine learning models. Over the past decade, researchers have designed many loss …

Most existing person re-identification (re-id) methods focus on learning the optimal distance
metrics across camera views. Typically a person's appearance is represented using features …

Abstract Integrated System Health Management (ISHM) is a promising technology that fuses
sensor data and historical state-of-health information of components and subsystems to …

It has been shown that the activations invoked by an image within the top layers of a large
convolutional neural network provide a high-level descriptor of the visual content of the …

Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional
data, due to their simple geometric interpretations and typically attractive computational …