Zhikui Chen

TL;DR: The emerging researches of deep learning models for big data feature learning are reviewed and the remaining challenges of big data deep learning are pointed out and the future topics are discussed.

TL;DR: This work studies a trajectory-based interaction time prediction algorithm to cope with an unstable network topology and high rate of disconnection in SIoVs and proposes a cooperative quality-aware system model, which focuses on a reliability assurance strategy and quality optimization method.

TL;DR: This review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multi-modality deep learning fusion method and to motivate new multimodAL data fusion techniques of deep learning.

TL;DR: To improve the efficiency of big data feature learning, the paper proposes a privacy preserving deep computation model by offloading the expensive operations to the cloud by using the BGV encryption scheme and employing cloud servers to perform the high-order back-propagation algorithm on the encrypted data efficiently forDeep computation model training.

TL;DR: An efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics and achieves a higher training efficiency and workload prediction accuracy than state-of-the-art machine-learning-based approaches.