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Momentum-incorporated Latent Factorization of Tensors for Extracting Temporal Patterns from QoS Data.

, , , and . SMC, page 1757-1762. IEEE, (2019)

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A Breif Review on Data-driven Battery Health Estimation Methods for Energy Storage Systems., and . ICNSC, page 1-6. IEEE, (2022)A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction., , , and . Neurocomputing, (2019)Momentum-incorporated Latent Factorization of Tensors for Extracting Temporal Patterns from QoS Data., , , and . SMC, page 1757-1762. IEEE, (2019)A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction., , , , , and . ICNSC, page 1-6. IEEE, (2020)Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data., , , , , and . IEEE Trans Autom. Sci. Eng., 18 (4): 2142-2155 (2021)MNL: A Highly-Efficient Model for Large-scale Dynamic Weighted Directed Network Representation., , and . IEEE Trans. Big Data, 9 (3): 889-903 (June 2023)An overview of data-driven battery health estimation technology for battery management system., , , , and . Neurocomputing, (May 2023)Efficient Representation to Dynamic QoS Data via Generalized Nesterov's Accelerated Gradient-incorporated Biased Non-negative Latent Factorization of Tensors., and . SMC, page 576-581. IEEE, (2021)Study on the Model of., , , , and . JCAI, page 363-366. IEEE Computer Society, (2009)