Results 181 to 190 of about 6,098 (213)
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AutoML for DenseNet Compression
2019DenseNet, which connects each convolutional layer to all preceding layers, is a classic model of utilizing skip connections to improve the performance and learning efficiency of deep convolutional neural networks. However, many of the skip connections in DenseNet are redundant, which may lead to huge consumption of computational resources and computing
Wencong Jiao +3 more
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Integrated Computer-Aided Engineering, 2022
Automated machine learning (AutoML) supports ML engineers and data scientist by automating single tasks like model selection and hyperparameter optimization, automatically generating entire ML pipelines. This article presents a survey of 20 state-of-the-art AutoML solutions, open source and commercial. There is a wide range of functionalities, targeted
Alexander Zender, Bernhard G. Humm
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Automated machine learning (AutoML) supports ML engineers and data scientist by automating single tasks like model selection and hyperparameter optimization, automatically generating entire ML pipelines. This article presents a survey of 20 state-of-the-art AutoML solutions, open source and commercial. There is a wide range of functionalities, targeted
Alexander Zender, Bernhard G. Humm
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CurL-AutoML: Curriculum Learning-based AutoML
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021Lucas Nildaimon dos Santos Silva +6 more
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2020
We can now create an efficient model using the techniques that were discussed in the previous chapters. Bayesian optimization goes a long way in finding hyperparameters. This chapter provides an overview of the Optuna framework and discusses further the role of hyperparameter optimization in automated machine learning.
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We can now create an efficient model using the techniques that were discussed in the previous chapters. Bayesian optimization goes a long way in finding hyperparameters. This chapter provides an overview of the Optuna framework and discusses further the role of hyperparameter optimization in automated machine learning.
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AutoML and Meta-learning for Multimedia
Proceedings of the 27th ACM International Conference on Multimedia, 2019AutoML and meta-learning are exciting and fast-growing research directions to the research community in both academia and industry. This tutorial is to disseminate and promote the recent research achievements on AutoML and meta-learning as well as their potential applications for multimedia.
Wenwu Zhu 0001 +2 more
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Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, 2023
Kevin Duh, Xuan Zhang
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Kevin Duh, Xuan Zhang
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Autoencoders and AutoML for intrusion detection
2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2023Alina Florina Glavan, Victor Croitoru
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Supplier classification by applying AutoML
2022 19th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 2022Daniela-Lizbeth Hernández-Sánchez +3 more
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Time Series Data Modeling Using Advanced Machine Learning and AutoML
Sustainability, 2022Celestine Iwendi +2 more
exaly