Spatially Explicit Hyperparameter Optimization for Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Spatially Explicit Hyperparameter Optimization for Neural Networks write by Minrui Zheng. This book was released on 2021-10-18. Spatially Explicit Hyperparameter Optimization for Neural Networks available in PDF, EPUB and Kindle. Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is written for researchers of the GIScience field as well as social science subjects.
Landslide: Susceptibility, Risk Assessment and Sustainability
Landslide: Susceptibility, Risk Assessment and Sustainability - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Landslide: Susceptibility, Risk Assessment and Sustainability write by Gopal Krishna Panda. This book was released on . Landslide: Susceptibility, Risk Assessment and Sustainability available in PDF, EPUB and Kindle.
AI 2023: Advances in Artificial Intelligence
AI 2023: Advances in Artificial Intelligence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook AI 2023: Advances in Artificial Intelligence write by Tongliang Liu. This book was released on 2023-11-26. AI 2023: Advances in Artificial Intelligence available in PDF, EPUB and Kindle. This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm.
ECAI 2020
ECAI 2020 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook ECAI 2020 write by G. De Giacomo. This book was released on 2020-09-11. ECAI 2020 available in PDF, EPUB and Kindle. This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Exploring the Use of Experimental Design Techniques for Hyperparameter Optimization in Convolutional Neural Networks
Exploring the Use of Experimental Design Techniques for Hyperparameter Optimization in Convolutional Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Exploring the Use of Experimental Design Techniques for Hyperparameter Optimization in Convolutional Neural Networks write by Ashley Chiu. This book was released on 2021. Exploring the Use of Experimental Design Techniques for Hyperparameter Optimization in Convolutional Neural Networks available in PDF, EPUB and Kindle. Deep learning techniques have become commonplace tools for complex prediction, classification, and recognition tasks. Yet, the performance of such learning techniques is highly influenced by user-set hyperparameters. As a result, efficient hyperparameter tuning and optimization is an increasingly important area of study. Traditional model-free tuning methods are often computationally inefficient and may miss optimal settings, while model-based approaches rely on parametric models and cannot easily be parallelized. In this thesis, we propose the use of experimental design techniques, a Design of Experiments (DOE) Approach, to more efficiently and intuitively optimize hyperparameters. We use fractional factorial designs, nearly orthogonal arrays, sliced Latin hypercube designs, and composite variations of these three small-run designs to identify relationships between continuous, discrete and categorical hyperparameters and test accuracy in convolutional neural networks using beta regression. We find that our proposed methodology successfully identifies optimal hyperparameter settings for convolutional neural networks trained on the MNIST dataset.
