LLR: Learning learning rates by LSTM for training neural networks
C Yu, X Qi, H Ma, X He, C Wang, Y Zhao - Neurocomputing, 2020 - Elsevier
… learning rate adjustment strategy is the adaptive learning rate, which means that during the
training process, different learning rates … the early work on adaptive learning rates. The three …
training process, different learning rates … the early work on adaptive learning rates. The three …
Wind speed ensemble forecasting based on deep learning using adaptive dynamic optimization algorithm
… a machine learning algorithm, called adaptive dynamic particle swarm algorithm (AD-PSO) …
dynamic process, the optimization algorithm starts to split agents of the population into two …
dynamic process, the optimization algorithm starts to split agents of the population into two …
Reconciling modern deep learning with traditional optimization analyses: The intrinsic learning rate
… training loss if the learning rate is set to be sufficiently small depending on the smoothness
constant and noise scale. In this viewpoint, if we reduce the learning rate … small learning rates …
constant and noise scale. In this viewpoint, if we reduce the learning rate … small learning rates …
Adaptive learning rate clipping stabilizes learning
JM Ede, R Beanland - … Learning: Science and Technology, 2020 - iopscience.iop.org
… with small batch sizes, high order loss functions or unstably high learning rates. To stabilize
learning, we have developed adaptive learning rate clipping (ALRC) to limit backpropagated …
learning, we have developed adaptive learning rate clipping (ALRC) to limit backpropagated …
A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things
… To propose MMLP for improving the effectiveness of the MLP using a set of multiple
networks and the back-propagation algorithm with an adaptive learning rate for classification of …
networks and the back-propagation algorithm with an adaptive learning rate for classification of …
A novel deep autoencoder and hyperparametric adaptive learning for imbalance intelligent fault diagnosis of rotating machinery
W Li, Z Shang, M Gao, S Qian, B Zhang… - … Applications of Artificial …, 2021 - Elsevier
… (3) A hyperparameters adaptive learning method of deep neural networks is proposed. ABC
… And adopt back-propagation algorithm and gradient descent algorithm to update the weight …
… And adopt back-propagation algorithm and gradient descent algorithm to update the weight …
Foal: Fast online adaptive learning for cardiac motion estimation
… Towards this end, we propose a fast online adaptive learning (FOAL) mechanism for dense
… an online adaptive stage and an offline meta-learning stage. The offline meta-learning trains …
… an online adaptive stage and an offline meta-learning stage. The offline meta-learning trains …
Auto-adaptive learning-based workload forecasting in dynamic cloud environment
… -based approaches allow the fast convergence, better exploration, learning capability and
more accuracy as compared to Backpropagation algorithm. The negative side is that they are …
more accuracy as compared to Backpropagation algorithm. The negative side is that they are …
Biphase adaptive learning-based neural network model for cloud datacenter workload forecasting
… learning algorithm that learns the suitable internal operators based on the data characteristics.
The extended algorithm is adaptive as it … The algorithm extends the adaptive learning of …
The extended algorithm is adaptive as it … The algorithm extends the adaptive learning of …
Machine learning-based boosted regression ensemble combined with hyperparameter tuning for optimal adaptive learning
… -making in predictive learning. Firstly, in this paper, a detailed performance benchmarking
of adaptive learning capacities of different key machine-learning-based regression models is …
of adaptive learning capacities of different key machine-learning-based regression models is …