Evolutionary Neural Architecture Search For Deep Learning
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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
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.
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.
Efficient Neural Architecture Search With Multiobjective Evolutionary Optimization
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Author : Maria Gabriela Baldeón Calisto
language : en
Publisher:
Release Date : 2020
Efficient Neural Architecture Search With Multiobjective Evolutionary Optimization written by Maria Gabriela Baldeón Calisto and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Diagnostic imaging categories.
Deep neural networks have become very successful at solving many complex tasks such as image classification, image segmentation, and speech recognition. These models are composed of multiple layers that have the capacity to learn increasingly higher-level features, without prior handcrafted specifications. However, the success of a deep neural network relies on finding the proper configuration for the task in hand. Given the vast number of hyperparameters and the massive search space, manually designing or fine-tuning deep learning architectures requires extensive knowledge, time, and computational resources. There is a growing interest in developing methods that automatically design a neural network ́s architecture, known as neural architecture search (NAS). NAS is usually modeled as a single-objective optimization problem where the aim is to find an architecture that maximizes the prediction ́s accuracy. However, most deep learning applications require accurate as well as efficient architectures to reduce memory consumption and enable their use in computationally-limited environments. This has led to the need to model NAS as a multiple objective problem that optimizes both the predictive performance and efficiency of the network. Furthermore, most NAS framework have focused on either optimizing the micro-structure (structure of the basic cell), or macro-structure (optimal number of cells and their connection) of the architecture. Consequently, manual engineering is required to find the topology of the non-optimized structure. Although NAS has demonstrated great potential in automatically designing an architecture, it remains a computationally expensive and time-consuming process because it requires training and evaluating many potential configurations. Recent work has focused on improving the search time of NAS algorithms, but most techniques have been developed and applied only for single-objective optimization problems. Given that optimizing multiple objectives has a higher complexity and requires more iterations to approximate the Pareto Front, it is critical to investigate algorithms that decrease the search time of multiobjective NAS. One critical application of deep learning is medical image segmentation. Segmentation of medical images provides valuable information for various critical tasks such as analyzing anatomical structures, monitoring disease progression, and predicting patient outcomes. Nonetheless, achieving accurate segmentation is challenging due to the inherent variability in appearance, shape, and location of the region of interest (ROI) between patients and the differences in imagining equipment and acquisition protocols. Therefore, neural networks are usually tailored to a specific application, anatomical region, and image modality. Moreover, medical image data is often volumetric requiring expensive 3D operations that result in large and complex architectures. Hence, training and deploying them requires considerable storage and memory bandwidth that makes them less suitable for clinical applications. To overcome these challenges, the main goal of this research is to automatically design accurate and efficient deep neural networks using multiobjective optimization algorithms for medical image segmentation. The proposed research consists of three major objectives: (1) to design a deep neural network that uses a multiobjective evolutionary based algorithm to automatically adapt to different medical image datasets while minimizing the model’s size; (2) to design a self-adaptive 2D-3D Fully Convolutional network (FCN) ensemble that incorporates volumetric information and optimizes both the performance and the size of the architecture; and (3) to design an efficient multiobjective neural architecture search framework that decreases the search time while simultaneously optimizing the micro- and macro-structure of the neural architecture. For the first objective, a multiobjective adaptive convolutional neural network named AdaResU-Net is presented for 2D medical image segmentation. The proposed AdaResU-Net is comprised of a fixed architecture and a learning framework that adjusts the hyperparameters to a particular training dataset using a multiobjective evolutionary based algorithm (MEA algorithm). The MEA algorithm evolves the AdaResU-Net network to optimize both the segmentation accuracy and model size. In the second objective, a self-adaptive ensemble of 2D-3D FCN named AdaEn-Net is proposed for 3D medical image segmentation. The AdaEn-Net is comprised of a 2D FCN that extracts intra-slice and long-range 2D context, and a 3D FCN architecture that exploits inter-slice and volumetric information. The 2D and 3D FCN architectures are automatically fitted for a specific medical image segmentation task by simultaneously optimizing the expected segmentation error and size of the network using the MEA algorithm. Finally, for the third objective, an efficient multiobjective neural architecture search framework named EMONAS is presented for 3D medical image segmentation. EMONAS has two main components, a novel search space that includes the hyperparameters that define the micro- and macro-structure of the architecture, and a Surrogate-assisted multiobjective evolutionary based algorithm (SaMEA algorithm) that efficiently searches for the best hyperparameter values using a Random Forest surrogate and guiding selection probabilities. The broader impact of the proposed research is as follows: (1) automating the design of deep neural networks’ architecture and hyperparameters to improve the performance and efficiency of the models; and (2) increase the accessibility of deep learning to a broader range of organizations and people by reducing the need of expert knowledge and GPU time when automatically designing deep neural networks. In the medical area, the proposed models aim to improve the automatic extraction of data from medical images to potentially enhance diagnosis, treatment planning and survival prediction of various diseases such as cardiac disease and prostate cancer. Although the proposed techniques are applied to medical image segmentation tasks, they can also be implemented in other applications where accurate and resource-efficient deep neural networks are needed such as autonomous navigation, augmented reality and internet-of-things.
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.
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 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.
Evolutionary Deep Learning
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Author : Michael Lanham
language : en
Publisher: Simon and Schuster
Release Date : 2023-07-18
Evolutionary Deep Learning written by Michael Lanham and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-18 with Computers categories.
Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser- known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. Google Colab notebooks make it easy to experiment and play around with each exciting example. By the time you’ve finished reading Evolutionary Deep Learning, you’ll be ready to build deep learning models as self-sufficient systems you can efficiently adapt to changing requirements. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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.
Automated Deep Learning Using Neural Network Intelligence
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Author : Ivan Gridin
language : en
Publisher: Apress
Release Date : 2022-06-21
Automated Deep Learning Using Neural Network Intelligence written by Ivan Gridin and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-21 with Computers categories.
Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level. What You Will Learn Know the basic concepts of optimization tuners, search space, and trials Apply different hyper-parameter optimization algorithms to develop effective neural networks Construct new deep learning models from scratch Execute the automated Neural Architecture Search to create state-of-the-art deep learning models Compress the model to eliminate unnecessary deep learning layers Who This Book Is For Intermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development
