Tucker-LSTM hybrid reduces MSE by 70.5% for EV battery SOC prediction versus standard LSTM on full-lifecycle field data.
Parallel adaptive stochastic gradient descent algorithms for latent factor analysis of high-dimensional and incomplete industrial data
2 Pith papers cite this work. Polarity classification is still indexing.
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BNBT combines block term tensor decomposition with linear bias terms and a new nonnegative update algorithm to outperform prior methods on real QoS datasets.
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A Hybrid Tucker-LSTM Tensor Network Model for SOC Prediction in Electric Vehicles
Tucker-LSTM hybrid reduces MSE by 70.5% for EV battery SOC prediction versus standard LSTM on full-lifecycle field data.
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A Biased Nonnegative Block Term Tensor Decomposition Model for Dynamic QoS Prediction
BNBT combines block term tensor decomposition with linear bias terms and a new nonnegative update algorithm to outperform prior methods on real QoS datasets.