Hierarchical reinforcement learning: A comprehensive survey
Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …
challenging long-horizon decision-making tasks into simpler subtasks. During the past …
Deep stochastic radar models
TA Wheeler, M Holder, H Winner… - 2017 IEEE Intelligent …, 2017 - ieeexplore.ieee.org
Accurate simulation and validation of advanced driver assistance systems requires accurate
sensor models. Modeling automotive radar is complicated by effects such as multipath …
sensor models. Modeling automotive radar is complicated by effects such as multipath …
Efficient memory management for gpu-based deep learning systems
GPU (graphics processing unit) has been used for many data-intensive applications. Among
them, deep learning systems are one of the most important consumer systems for GPU …
them, deep learning systems are one of the most important consumer systems for GPU …
[图书][B] Stochastic methods for modeling and predicting complex dynamical systems: uncertainty quantification, state estimation, and reduced-order models
N Chen - 2023 - books.google.com
This book enables readers to understand, model, and predict complex dynamical systems
using new methods with stochastic tools. The author presents a unique combination of …
using new methods with stochastic tools. The author presents a unique combination of …
A hybrid physics-based and stochastic neural network model structure for diesel engine combustion events
K Ankobea-Ansah, CM Hall - Vehicles, 2022 - mdpi.com
Estimation of combustion phasing and power production is essential to ensuring proper
combustion and load control. However, archetypal control-oriented physics-based …
combustion and load control. However, archetypal control-oriented physics-based …
Markov chain neural networks
M Awiszus, B Rosenhahn - Proceedings of the IEEE …, 2018 - openaccess.thecvf.com
In this work we present a modified neural network model which is capable to simulate
Markov Chains. We show how to express and train such a network, how to ensure given …
Markov Chains. We show how to express and train such a network, how to ensure given …
Stochastic spintronic neuron with application to image binarization
The hardware implementation of neural network has always been of interest to the
researchers as it can significantly increase the efficiency and application of neural networks …
researchers as it can significantly increase the efficiency and application of neural networks …
Applications of business analytics in predicting flight on-time performance in a complex and dynamic system
Flight on-time performance is one of the most important issues in the National Airspace
System, a very complex and dynamic system. To avoid negative impacts to the aviation …
System, a very complex and dynamic system. To avoid negative impacts to the aviation …
Application of reinforcement learning for intelligent support decision system: A paradigm towards safety and explainability
C Maiuri, M Karimshoushtari, F Tango… - … Conference on Human …, 2023 - Springer
Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. In
particular, when AI is combined with the rapid development of mobile communication and …
particular, when AI is combined with the rapid development of mobile communication and …
Controlling mean exit time of stochastic dynamical systems based on quasipotential and machine learning
The mean exit time escaping basin of attraction in the presence of white noise is of practical
importance in various scientific fields. In this work, we propose a strategy to control mean …
importance in various scientific fields. In this work, we propose a strategy to control mean …