Efficient Neural Architecture Generation With An Invertible Neural Network For Neural Architecture Search
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Efficient Neural Architecture Generation With An Invertible Neural Network For Neural Architecture Search
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Author : 陳冠頴
language : en
Publisher:
Release Date : 2023
Efficient Neural Architecture Generation With An Invertible Neural Network For Neural Architecture Search written by 陳冠頴 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Deep Dive Into The Application Of Aggregated Invertible Neural Network As Performance Predictor In Neural Architecture Search
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Author : 楊大煒
language : en
Publisher:
Release Date : 2024
Deep Dive Into The Application Of Aggregated Invertible Neural Network As Performance Predictor In Neural Architecture Search written by 楊大煒 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
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 And Practical Neural Architecture Search
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Author : Thomas Elsken
language : en
Publisher:
Release Date : 2021
Efficient And Practical Neural Architecture Search written by Thomas Elsken 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 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.
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.
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.
Efficient Neural Architecture Search Using A Genetic Algorithm
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Author : Brandon S. Morgan
language : en
Publisher:
Release Date : 2022
Efficient Neural Architecture Search Using A Genetic Algorithm written by Brandon S. Morgan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Genetic algorithms categories.
Efficient Neural Network Architecture Search
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Author : Roxana Istrate
language : en
Publisher:
Release Date : 2019
Efficient Neural Network Architecture Search written by Roxana Istrate and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Hands On Mathematics For Deep Learning
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Author : Jay Dawani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-06-12
Hands On Mathematics For Deep Learning written by Jay Dawani and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-12 with Computers categories.
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
