In recent years, with the great success of deep learning and especially deep unsupervised
learning, many deep architectural clustering methods, collectively known as deep clustering …

Generative adversarial networks (or GANs) are a specific deep learning architecture often
used for different usages, such as data generation or image-to-image translation. In recent …

In this article, a new ensemble learning model called GATE is proposed to improve the
accuracy and stability of time-series forecasting, which is a crucial aspect of modern …

The application of clustering has always been an important method for problem-solving. In
the era of big data, most classical clustering methods suffer from the curse of dimensionality …

Imitation learning aims to reproduce expert behaviors without relying on an explicit reward
signal. However, real-world demonstrations often present challenges, such as multi-modal …

In deep learning, the quality of ground truth training data is crucial for the resulting
performance. However, depending on applications, collecting a sufficient amount of quality …

The field of autonomous vehicles (AVs) predominantly leverages multi-modal integration of
LiDAR and camera data to achieve better performance compared to using a single modality …

Seismic inversion is crucial in hydrocarbon exploration, particularly for detecting
hydrocarbons in thin layers. However, the detection of sparse thin layers within seismic …

In real world scenarios, out-of-distribution (OOD) datasets may have a large distributional
shift from training datasets. This phenomena generally occurs when a trained classifier is …

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