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58 Result(s)
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Yan** Sun
Artificial Intelligence
Computational Intelligence
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Chapter
Evolutionary Neural Network Architecture Search
Deep Neural Networks (DNNs) have been remarkably successful in numerous scenarios of machine learning. However, the typical design for DNN architectures is manual, which highly relies on the domain knowledge a...
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Article
Structured products dynamic hedging based on reinforcement learning
In the Black–Scholes model proposed in 1973, an investor can use a continuously rebalanced dynamic strategy to hedge the risk of a certain option, assuming that the underlying asset’s price is subject to geome...
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Article
Asymmetric ranging algorithm based on signal emergence angle for underwater wireless sensor network
In underwater wireless sensor networks, distance-related localisation technologies rely on acquiring distance information between nodes to complete node location. Currently, ranging algorithms generally have t...
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Book
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Chapter
Evolutionary Computation
EC is a class of nature-inspired algorithms that maintains a population of candidate solutions (individuals) and evolves toward the best answer(s). It has been frequently used to solve difficult real-world opt...
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Chapter
Differential Evolution for Architecture Design
The general goal of this chapter is to explore the capacity of DE, named DECNN, to evolve deep CNN architectures and parameters automatically. Designing new crossover and mutation operators of DE, as well as a...
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Chapter
Hybrid GA and PSO for Architecture Design
In this chapter, a new approach based on EC is introduced for automatically searching for the optimal CNN architecture and determining whether or not to use shortcut connections between one layer and its forwa...
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Chapter
Encoding Space Based on Directed Acyclic Graphs
Although CNN-GA [1] is totally automated, the generated architectures have a restricted connectional structure since it employs an encoding strategy that encodes the building blocks into a linked list that can be...
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Chapter and Conference Paper
Ship Target Detection in Remote Sensing Image Based on Improved RetinaNet
Ship image target detection has important applications for ship management. In recent years, target detection based on deep learning has been widely studied in visual ship target detection. However, due to the...
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Chapter
Internet Protocol Based Architecture Design
There has been a large number of research done to enhance utilizing EC for an evolved CNN architecture, but there has not been much study on using other EC approaches to develop CNN architectures, as a result,...
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Chapter
Architecture Design for Analyzing Hyperspectral Images
Denoising images is a key part of the process of images. The hyperspectral image (HSI) has three dimensions in addition to the natural 2D image to display spectral and spatial information. In forestry [2, 2], agr...
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Chapter
Deep Neural Networks
The neural networks (NNs) with deep architectures are referred to as DNNs. In general, there is no universal standard of how deep a CNN must be to be considered deep. In practice, a DNN is defined as a NN with...
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Chapter
Architecture Design for Convolutional Auto-Encoders
Although the CAE and its variations have proven benefits in a variety of applications, one key restriction is that their stacked architectures are incompatible with those of state-of-the-art CNNs. The amount o...
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Chapter
Architecture Design for Plain CNNs
Using GAs in the architecture design of CNNs directly presents a number of challenges. On the one hand, the suitable architecture cannot be determined unless its performance is analyzed and compared to other a...
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Chapter
Architecture Design for Skip-Connection Based CNNs
In this chapter, an efficient and effective algorithms employing GA is introduced, dubbed CNN-GA, to find the best CNN architectures for specific image classification tasks automatically, such that the found C...
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Chapter
Deep Neural Architecture Pruning
The majority of NAS algorithms are intended to identify the optimal CNN architectures for a specific task. CNNs utilized for image classification and recognition problems demand strong hardware, such as data c...
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Chapter
Distribution Training Framework for Architecture Design
As discussed in Part I, for the time-consuming issue of the ENAS methods, there are two primary categories of available acceleration methods. First, various acceleration approaches for DNN evaluation are propo...
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Chapter
Architecture Design for Stacked AEs and DBNs
As introduced in Part II, altering n in Eq. (1) could learn numerous different representations, but only those that perform exceptionally well on the machine learning tasks linked with them are given attention.
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Chapter
Architecture Design for Variational Auto-Encoders
Most VAEs were developed with symmetrical architecture in mind, which means that the encoder and decoder must have the same number of layers. However, when completing the step of unsupervised pre-training step...
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Chapter
Architecture Design for RBs and DBs Based CNNs
As mentioned in Part III, the research of CNN architectural design algorithms is now in the early phases, particularly for entirely automatic ones with great performance and using limited CPU resources. In thi...
