A Learning‐Rate Modulable and Reliable TiO x Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing
Sign Up to like & getrecommendations! Published in 2022 at "Advanced Science"
DOI: 10.1002/advs.202201117
Abstract: Realization of memristor‐based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system‐level. In this sense, uniform and reliable titanium oxide (TiOx) memristor array devices are fabricated… read more here.
Keywords: hardware; memristor array; learning rate; memristor ... See more keywords
An automatic learning rate decay strategy for stochastic gradient descent optimization methods in neural networks
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22883
Abstract: Stochastic Gradient Descent (SGD) series optimization methods play the vital role in training neural networks, attracting growing attention in science and engineering fields of the intelligent system. The choice of learning rates affects the convergence… read more here.
Keywords: neural networks; optimization methods; learning rate; rate decay ... See more keywords
An adaptive mechanism to achieve learning rate dynamically
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3495-0
Abstract: Gradient descent is prevalent for large-scale optimization problems in machine learning; especially it nowadays plays a major role in computing and correcting the connection strength of neural networks in deep learning. However, many gradient-based optimization… read more here.
Keywords: mechanism; exponential decay; adaptive mechanism; rate ... See more keywords
Effects of OCRA parameters and learning rate on machine scheduling
Sign Up to like & getrecommendations! Published in 2020 at "Central European Journal of Operations Research"
DOI: 10.1007/s10100-020-00708-3
Abstract: In this paper, the effects of Occupational Repetitive Actions (OCRA) parameters, learning rate on process times, and machine scheduling were investigated. We propose that Work-Related Musculoskeletal Disorder (WMSD) risks should be taken into account in… read more here.
Keywords: machine; ocra parameters; machine scheduling; wmsd risks ... See more keywords
Wear indicator construction of rolling bearings based on multi-channel deep convolutional neural network with exponentially decaying learning rate
Sign Up to like & getrecommendations! Published in 2019 at "Measurement"
DOI: 10.1016/j.measurement.2018.11.040
Abstract: Abstract Wear indicators (WIs) attempt to identify historical and ongoing degradation processes by extracting features from acquired data. The quality of the constructed WIs affects the validity of the data-driven prediction directly to a great… read more here.
Keywords: construction; neural network; multi channel; learning rate ... See more keywords
An astonishing regularity in student learning rate
Sign Up to like & getrecommendations! Published in 2023 at "Proceedings of the National Academy of Sciences of the United States of America"
DOI: 10.1073/pnas.2221311120
Abstract: Significance Prior research, often using self-report data, hypothesizes that the path to expertise requires extensive practice and that different learners acquire competence at different rates. Fitting cognitive and statistical growth models to 27 datasets involving… read more here.
Keywords: learning rate; regularity student; rate; student learning ... See more keywords
Learning rate and subjective mental workload in five truck driving tasks
Sign Up to like & getrecommendations! Published in 2019 at "Ergonomics"
DOI: 10.1080/00140139.2018.1545054
Abstract: Abstract Both learning curve models and subjective mental workload are useful tools for determining the length of training for new workers and predicting future task performance. An experiment was designed to collect the task completion… read more here.
Keywords: workload; mental workload; subjective mental; learning rate ... See more keywords
Adaptive latent state modeling of brain network dynamics with real-time learning rate optimization
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/abcefd
Abstract: Objective. Dynamic latent state models are widely used to characterize the dynamics of brain network activity for various neural signal types. To date, dynamic latent state models have largely been developed for stationary brain network… read more here.
Keywords: state; brain; brain network; learning rate ... See more keywords
Predator or provider? How wild animals respond to mixed messages from humans
Sign Up to like & getrecommendations! Published in 2022 at "Royal Society Open Science"
DOI: 10.1098/rsos.211742
Abstract: Wild animals encounter humans on a regular basis, but humans vary widely in their behaviour: whereas many people ignore wild animals, some people present a threat, while others encourage animals' presence through feeding. Humans thus… read more here.
Keywords: wild animals; provider wild; mixed messages; animals respond ... See more keywords
A Batch Variable Learning Rate Gradient Descent Algorithm With the Smoothing L1/2 Regularization for Takagi-Sugeno Models
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2997867
Abstract: A batch variable learning rate gradient descent algorithm is proposed to efficiently train a neuro-fuzzy network of zero-order Takagi-Sugeno inference systems. By using the advantages of regularization, the smoothing $L_{1/2}$ regularization is utilized to find… read more here.
Keywords: rate; rate gradient; algorithm; learning rate ... See more keywords
PACL: Piecewise Arc Cotangent Decay Learning Rate for Deep Neural Network Training
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3002884
Abstract: Deep neural networks (DNNs) are currently the best-performing method for many classification problems. For training DNNs, the learning rate is the most important hyper-parameter, choice of which affects the performance of the model greatly. In… read more here.
Keywords: piecewise arc; deep neural; rate; cotangent decay ... See more keywords