Evolutionary Neural Architecture Search For Transfer Learning
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Evolutionary Neural Architecture Search For Transfer Learning
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Author : Sheng-Hsuan Peng
language : en
Publisher:
Release Date : 2021
Evolutionary Neural Architecture Search For Transfer Learning written by Sheng-Hsuan Peng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Data Driven Evolutionary Optimization
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Author : Yaochu Jin
language : en
Publisher: Springer Nature
Release Date : 2021-06-28
Data Driven Evolutionary Optimization written by Yaochu Jin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-28 with Computers categories.
Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Evolutionary Neural Architecture Search For Deep Learning
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Author : Jason Zhi Liang
language : en
Publisher:
Release Date : 2018
Evolutionary Neural Architecture Search For Deep Learning written by Jason Zhi Liang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. DNNs are often not used to their full potential because it is difficult to determine what architectures and hyperparameters should be used. While several approaches have been proposed, computational complexity of searching large design spaces makes them impractical for large modern DNNs. This dissertation introduces an efficient evolutionary algorithm (EA) for simultaneous optimization of DNN architecture and hyperparameters. It builds upon extensive past research of evolutionary optimization of neural network structure. Various improvements to the core algorithm are introduced, including: (1) discovering DNN architectures of arbitrary complexity; (1) generating modular, repetitive modules commonly seen in state-of-the-art DNNs; (3) extending to the multitask learning and multiobjective optimization domains; (4) maximizing performance and reducing wasted computation through asynchronous evaluations. Experimental results in image classification, image captioning, and multialphabet character recognition show that the approach is able to evolve networks that are competitive with or even exceed hand-designed networks. Thus, the method enables an automated and streamlined process to optimize DNN architectures for a given problem and can be widely applied to solve harder tasks
Automated Machine Learning
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Author : Frank Hutter
language : en
Publisher: Springer
Release Date : 2019-05-17
Automated Machine Learning written by Frank Hutter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-17 with Computers categories.
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Evolutionary Multi Objective Bi Level Optimization For Efficient Deep Neural Network Architecture Design
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Author : Zhichao Lu
language : en
Publisher:
Release Date : 2020
Evolutionary Multi Objective Bi Level Optimization For Efficient Deep Neural Network Architecture Design written by Zhichao Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic dissertations categories.
Deep convolutional neural networks (CNNs) are the backbones of deep learning (DL) paradigms for numerous vision tasks, including object recognition, detection, segmentation, etc. Early advancements in CNN architectures are primarily driven by human expertise and elaborate design. Recently, neural architecture search (NAS) was proposed with the aim of automating the network design process and generating task-dependent architectures. While existing approaches have achieved competitive performance, they are still impractical to real-world deployment for three reasons: (1) the generated architectures are solely optimized for predictive performance, resulting in inefficiency in utilizing hardware resources---i.e. energy consumption, latency, memory size, etc.; (2) the search processes require vast computational resources in most approaches; (3) most existing approaches require one complete search for each deployment specification of hardware or requirement. In this dissertation, we propose an efficient evolutionary NAS algorithm to address the aforementioned limitations. In particular, we first introduce Pareto-optimization to NAS, leading to a diverse set of architectures, trading-off multiple objectives, being obtained simultaneously in one run. We then improve the algorithm's search efficiency through surrogate models. We finally integrate a transfer learning scheme to the algorithm that allows a new task to leverage previous search efforts that further improves both the performance of the obtained architectures and search efficiency. Therefore, the proposed algorithm enables an automated and streamlined process to efficiently generate task-specific custom neural network models that are competitive under multiple objectives.
Deep Neural Evolution
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Author : Hitoshi Iba
language : en
Publisher: Springer Nature
Release Date : 2020-05-20
Deep Neural Evolution written by Hitoshi Iba and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-20 with Computers categories.
This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research —from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.
Optinformatics In Evolutionary Learning And Optimization
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Author : Liang Feng
language : en
Publisher: Springer Nature
Release Date : 2021-03-29
Optinformatics In Evolutionary Learning And Optimization written by Liang Feng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-29 with Technology & Engineering categories.
This book provides readers the recent algorithmic advances towards realizing the notion of optinformatics in evolutionary learning and optimization. The book also provides readers a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning. Through reading this book, the readers will understand the concept of optinformatics, recent research progresses in this direction, as well as particular algorithm designs and application of optinformatics. Evolutionary algorithms (EAs) are adaptive search approaches that take inspiration from the principles of natural selection and genetics. Due to their efficacy of global search and ease of usage, EAs have been widely deployed to address complex optimization problems occurring in a plethora of real-world domains, including image processing, automation of machine learning, neural architecture search, urban logistics planning, etc. Despite the success enjoyed by EAs, it is worth noting that most existing EA optimizers conduct the evolutionary search process from scratch, ignoring the data that may have been accumulated from different problems solved in the past. However, today, it is well established that real-world problems seldom exist in isolation, such that harnessing the available data from related problems could yield useful information for more efficient problem-solving. Therefore, in recent years, there is an increasing research trend in conducting knowledge learning and data processing along the course of an optimization process, with the goal of achieving accelerated search in conjunction with better solution quality. To this end, the term optinformatics has been coined in the literature as the incorporation of information processing and data mining (i.e., informatics) techniques into the optimization process. The primary market of this book is researchers from both academia and industry, who are working on computational intelligence methods and their applications. This book is also written to be used as a textbook for a postgraduate course in computational intelligence emphasizing methodologies at the intersection of optimization and machine learning.
Neural Architecture Search Using Efficient Evolutionary Algorithms
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Author :
language : en
Publisher:
Release Date : 2022
Neural Architecture Search Using Efficient Evolutionary Algorithms written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Evolutionary Deep Neural Architecture Search Fundamentals Methods And Recent Advances
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Author : Yanan Sun
language : en
Publisher: Springer Nature
Release Date : 2022-11-08
Evolutionary Deep Neural Architecture Search Fundamentals Methods And Recent Advances written by Yanan Sun and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-08 with Technology & Engineering categories.
This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.
Federated And Transfer Learning
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Author : Roozbeh Razavi-Far
language : en
Publisher: Springer Nature
Release Date : 2022-09-30
Federated And Transfer Learning written by Roozbeh Razavi-Far and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-30 with Technology & Engineering categories.
This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
