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Coupling Effect of Exploration Rate and Learning Rate for Optimized Scaled Reinforcement Learning
Reinforcement learning (RL) is making paradigm transformations in artificial intelligence frameworks in the field of autonomous robotics. This...
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A Modified Stochastic Gradient Descent Optimization Algorithm With Random Learning Rate for Machine Learning and Deep Learning
An optimization algorithm is essential for minimizing loss (or objective) functions in machine learning and deep learning. Optimization algorithms...
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MABSearch: The Bandit Way of Learning the Learning Rate—A Harmony Between Reinforcement Learning and Gradient Descent
Gradient descent (GD) is an elementary optimization (OP) algorithm quite well-known in machine learning. It is the preferred choice of the OP...
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Hermite broad-learning recurrent neural control with adaptive learning rate for nonlinear systems
Although conventional control systems are simple and widely used, they may not be effective for complex and uncertain systems. This study proposes a...
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Facial Micro-expression Modelling-Based Student Learning Rate Evaluation Using VGG–CNN Transfer Learning Model
Micro-facial expressions hold the potential to identify emotional states of students during their participation in online learning tasks. Through...
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Kronecker-factored Approximate Curvature with adaptive learning rate for optimizing model-agnostic meta-learning
Model-agnostic meta-learning (MAML) highlights the ability to quickly adapt to new tasks with only a small amount of labeled training data among many...
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Control learning rate for autism facial detection via deep transfer learning
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects social interaction and communication. Early detection of ASD can...
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To raise or not to raise: the autonomous learning rate question
There is a parameter ubiquitous throughout the deep learning world: learning rate. There is likewise a ubiquitous question: what should that learning...
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Visual perceptual learning modulates microsaccade rate and directionality
Microsaccades, incessant “fixational eye movements” (< 1°), are an important window into cognitive functions. Yet, its role in visual perceptual...
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An Adaptive Learning Rate Deep Learning Optimizer Using Long and Short-Term Gradients Based on G–L Fractional-Order Derivative
Deep learning model is a multi-layered network structure, and the network parameters that evaluate the final performance of the model must be trained...
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Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies
Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants (WWTPs), as it is closely related to...
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Deep-learning based sleep apnea detection using sleep sound, SpO2, and pulse rate
Sleep apnea, a common sleep disorder where breathing is repeatedly interrupted during sleep, poses significant health risks. Traditional diagnostic...
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Mesolimbic dopamine adapts the rate of learning from action
Recent success in training artificial agents and robots derives from a combination of direct learning of behavioural policies and indirect learning...
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Reducing dropout rate through a deep learning model for sustainable education: long-term tracking of learning outcomes of an undergraduate cohort from 2018 to 2021
In recent years, initiatives and the resulting application of precision education have been applied with increasing frequency in Taiwan; the...
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Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning
The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to...
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Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking
The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the...
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GLR: Gradient-Based Learning Rate Scheduler
Training a neural network is a complex and time-consuming process because of many combinations of hyperparameters that have to be adjusted and...
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Heuristic Technique to Find Optimal Learning Rate of LSTM for Predicting Student Dropout Rate
Predictive analytics is being increasingly recognized as being important for evaluating university students’ academic achievement. Utilizing big data...
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A simple method for measuring pollen germination rate using machine learning
The pollen germination rate decreases under various abiotic stresses, such as high-temperature stress, and it is one of the causes of inhibition of...
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Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach
In this study, we investigate and apply the models from the machine learning (ML) paradigm to forecast the inflation rate. The models identified are...
