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Learning to optimize: A tutorial for continuous and mixed-integer optimization
Learning to optimize (L2O) stands at the intersection of traditional optimization and machine learning, utilizing the capabilities of machine...
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Control Policy Learning Design for Vehicle Urban Positioning via BeiDou Navigation
This paper presents a learning-based control policy design for point-to-point vehicle positioning in the urban environment via BeiDou navigation....
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Meta Algorithms for Portfolio Optimization Using Reinforcement Learning
We explore the effectiveness of various machine learning algorithms, especially deep reinforcement learning, for solving the portfolio optimization...
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Learning fine-grained search space pruning and heuristics for combinatorial optimization
Combinatorial optimization problems arise naturally in a wide range of applications from diverse domains. Many of these problems are NP-hard and...
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Introductory Material to Animation and Learning
In this chapter, we introduce concepts in computer animation, starting with physics-based animation. We revise the main steps of the pipeline...
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Learning to sample initial solution for solving 0–1 discrete optimization problem by local search
Local search methods are convenient alternatives for solving discrete optimization problems (DOPs). These easy-to-implement methods are able to find...
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A Survey on Deep Learning-Based Diffeomorphic Map**
Diffeomorphic map** is a specific type of registration methods that can be used to align biomedical structures for subsequent analyses....
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A projected primal-dual gradient optimal control method for deep reinforcement learning
In this contribution, we start with a policy-based Reinforcement Learning ansatz using neural networks. The underlying Markov Decision Process...
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Linear-Quadratic Stochastic Delayed Control and Deep Learning Resolution
We consider a simple class of stochastic control problems with a delayed control, in both the drift and the diffusion part of the state stochastic...
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Deep Graph Machine Learning Models for Epidemic Spread Prediction and Prevention
Epidemic spread prediction and prevention have been of paramount significance for safeguarding the public health and quality of life. However, the...
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Soybean Price Trend Forecast Using Deep Learning Techniques Based on Prices and Text Sentiments
Predicting product prices is an essential activity in agricultural value chains. It can improve decision making and revenues for all agents. This...
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Learning Scalable Task Assignment with Imperative-Priori Conflict Resolution in Multi-UAV Adversarial Swarm Defense Problem
The multi-UAV adversary swarm defense (MUASD) problem is to defend a static base against an adversary UAV swarm by a defensive UAV swarm. Decomposing...
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Machine Learning for Quantum Control
This chapter presents results on learning controlLearning control of quantum systems. In Sect. 5.2, two differential evolutionDifferential...
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A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot
It is a significant research direction for highly complex musculoskeletal robots that how to develop the ability of motion learning and...
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Reinforcement Learning for the Knapsack Problem
Combinatorial optimization (CO) problems are at the heart of both practical and theoretical research. Due to their complexity, many problems cannot...
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Space-time error estimates for deep neural network approximations for differential equations
Over the last few years deep artificial neural networks (ANNs) have very successfully been used in numerical simulations for a wide variety of...
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Pretty Darn Good Control: When are Approximate Solutions Better than Approximate Models
Existing methods for optimal control struggle to deal with the complexity commonly encountered in real-world systems, including dimensionality,...
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An Ontology-Based Approach for Making Smart Suggestions Based on Sequence-Based Context Modeling and Deep Learning Classifications
The aim of the study is to identify knowledge gaps and prospects in the tourism industry for building interactive hybrid recommender systems that...
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Mathematical methods for maintenance and operation cost prediction based on transfer learning in State Grid
The electric power enterprise is an important basic energy industry for national development, and it is also the first basic industry of the national...
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Learning the flux and diffusion function for degenerate convection-diffusion equations using different types of observations
In recent years, there has been an increasing interest in utilizing deep learning-based techniques to predict solutions to various partial...
