Towards robust prediction on tail labels
Extreme multi-label learning (XML) works to annotate objects with relevant labels from an
extremely large label set. Many previous methods treat labels uniformly such that the …
extremely large label set. Many previous methods treat labels uniformly such that the …
Urban mobility analytics: A deep spatial–temporal product neural network for traveler attributes inference
This study examines the potential of using smart card data in public transit systems to infer
attributes of travelers, thereby facilitating a more user-centered public transport service …
attributes of travelers, thereby facilitating a more user-centered public transport service …
How does augmented observation facilitate multimodal representational thinking? Applying deep learning to decode complex student construct
In this paper, we demonstrate how machine learning could be used to quickly assess a
student's multimodal representational thinking. Multimodal representational thinking is the …
student's multimodal representational thinking. Multimodal representational thinking is the …
A survey of deep network techniques all classifiers can adopt
Deep neural networks (DNNs) have introduced novel and useful tools to the machine
learning community. Other types of classifiers can potentially make use of these tools as well …
learning community. Other types of classifiers can potentially make use of these tools as well …
Automatic multi-label ECG classification with category imbalance and cost-sensitive thresholding
Automatic electrocardiogram (ECG) classification is a promising technology for the early
screening and follow-up management of cardiovascular diseases. It is, by nature, a multi …
screening and follow-up management of cardiovascular diseases. It is, by nature, a multi …
Hot-vae: Learning high-order label correlation for multi-label classification via attention-based variational autoencoders
Understanding how environmental characteristics affect biodiversity patterns, from individual
species to communities of species, is critical for mitigating effects of global change. A central …
species to communities of species, is critical for mitigating effects of global change. A central …
Cifdm: continual and interactive feature distillation for multi-label stream learning
Multi-label learning algorithms have attracted more and more attention as of recent. This is
mainly because real-world data is generally associated with multiple and non-exclusive …
mainly because real-world data is generally associated with multiple and non-exclusive …
Learning dual low-rank representation for multi-label micro-video classification
Currently, with the rapid development of mobile Internet, micro-video has become a
prevailing format of user-generated contents (UGCs) on various social media platforms …
prevailing format of user-generated contents (UGCs) on various social media platforms …
Compact learning for multi-label classification
Multi-label classification (MLC) studies the problem where each instance is associated with
multiple relevant labels, which leads to the exponential growth of output space. It confronts …
multiple relevant labels, which leads to the exponential growth of output space. It confronts …
Multilabel remote sensing image annotation with multiscale attention and label correlation
R Huang, F Zheng, W Huang - IEEE Journal of Selected Topics …, 2021 - ieeexplore.ieee.org
Deep-learning-based multilabel image annotation is receiving increasing attention in the
field of remote sensing due to the great success of deep networks in single-label remote …
field of remote sensing due to the great success of deep networks in single-label remote …