T Yu, H Zhu - arXiv preprint arXiv:2003.05689, 2020 - arxiv.org
Since deep neural networks were developed, they have made huge contributions to everyday lives. Machine learning provides more rational advice than humans are capable of …
M Feurer, F Hutter - Automated machine learning: Methods …, 2019 - library.oapen.org
Recent interest in complex and computationally expensive machine learning models with many hyperparameters, such as automated machine learning (AutoML) frameworks and …
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on …
Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial …
Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect …
PI Frazier - arXiv preprint arXiv:1807.02811, 2018 - arxiv.org
Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimization …
Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge-and labor-intensive to pursue good learning performance, humans are heavily …
MA Zöller, MF Huber - Journal of artificial intelligence research, 2021 - jair.org
Abstract Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly …