Sensor fusion is essential for autonomous driving and autonomous robots,...
Advances in autonomous driving are inseparable from sensor fusion.
Heter...
Tensor train (TT) representation has achieved tremendous success in visu...
In this paper, we explain the inference logic of large language models (...
Multi-task learning (MTL) aims at solving multiple related tasks
simulta...
Gaussian process state-space model (GPSSM) is a fully probabilistic
stat...
Sequential data naturally have different lengths in many domains, with s...
Sparse modeling for signal processing and machine learning has been at t...
Self-Attention is a widely used building block in neural modeling to mix...
Recently, there is a revival of interest in low-rank matrix completion-b...
Neural networks often encounter various stringent resource constraints w...
Classification of long sequential data is an important Machine Learning ...
Training a machine learning model with federated edge learning (FEEL) is...
Cross-domain recommendation can help alleviate the data sparsity issue i...
Square matrices appear in many machine learning problems and models.
Opt...
This paper investigates the problem of joint massive devices separation ...
To prevent the leakage of private information while enabling automated
m...
Edge learning (EL), which uses edge computing as a platform to execute
m...
Tensor rank learning for canonical polyadic decomposition (CPD) has long...
This paper studies the massive machine-type communications (mMTC) for th...
One key feature of massive multiple-input multiple-output systems is the...
A crucial challenge in image-based modeling of biomedical data is to ide...