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AMLB: an AutoML Benchmark [article]

Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren
2022 arXiv   pre-print
We conduct a thorough comparison of 9 well-known AutoML frameworks across 71 classification and 33 regression tasks.  ...  The benchmark comes with an open-source tool that integrates with many AutoML frameworks and automates the empirical evaluation process end-to-end: from framework installation and resource allocation to  ...  We also thank Rinchin Damdinov, Nick Erickson, Matthias Feurer, and Piotr P loński for feedback and corrections to this manuscript.  ... 
arXiv:2207.12560v1 fatcat:sowl2nle3bfajai3yp4k6eabeu

Benchmark and Survey of Automated Machine Learning Frameworks [article]

Marc-André Zöller, Marco F. Huber
2021 arXiv   pre-print
Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics  ...  Machine learning (ML) has become a vital part in many aspects of our daily life.  ...  Lately, several surveys regarding AutoML have been published.  ... 
arXiv:1904.12054v5 fatcat:3gbpofwnl5a3zduqr5vportaly

Benchmark and Survey of Automated Machine Learning Frameworks

Marc-André Zöller, Marco F. Huber
2021 The Journal of Artificial Intelligence Research  
Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics  ...  Machine learning (ML) has become a vital part in many aspects of our daily life.  ...  Lately, several surveys regarding AutoML have been published.  ... 
doi:10.1613/jair.1.11854 fatcat:whi5mdcidrfffmid6mabprzug4

A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters [article]

Pierrick Pochelu, Serge G. Petiton, Bruno Conche
2022 arXiv   pre-print
Automated Machine Learning with ensembling (or AutoML with ensembling) seeks to automatically build ensembles of Deep Neural Networks (DNNs) to achieve qualitative predictions.  ...  While previous works on AutoML focus to search for the best model to maximize its generalization ability, we rather propose a new AutoML to build a larger library of accurate and diverse individual models  ...  We would like to thank TotalEnergies SE and its subsidiaries for allowing us to share this material and make available the needed resources.  ... 
arXiv:2208.14046v1 fatcat:cisw4uh2x5fsbdi3edi6cncrtm

AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning [article]

Keting Lu, Shiqi Zhang, Xiaoping Chen
2020 arXiv   pre-print
Results collected from a set of six robotic control environments show that, in comparison to a standard deep RL algorithm (DDPG), AutoEG increases the speed of learning process by at least 30%.  ...  Focusing on addressing this limitation, this paper makes a twofold contribution.  ...  Automated machine learning (AutoML) recently has emerged as a new area in the machine learning community [Quanming et al., 2018 ].  ... 
arXiv:2004.10698v2 fatcat:ufooi5hfujafnfosasauhvdla4

AutonoML: Towards an Integrated Framework for Autonomous Machine Learning [article]

David Jacob Kedziora and Katarzyna Musial and Bogdan Gabrys
2022 arXiv   pre-print
Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their  ...  In doing so, we survey developments in the following research areas: hyperparameter optimisation, multi-component models, neural architecture search, automated feature engineering, meta-learning, multi-level  ...  Naive Bayes classifiers. By the late 1990s, feature-selection methods were roughly categorised under 'filter' and 'wrapper' approaches [211] .  ... 
arXiv:2012.12600v2 fatcat:6rj4ubhcjncvddztjs7tql3itq

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Sebastian Raschka, Joshua Patterson, Corey Nolet
2020 Information  
This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it.  ...  We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.  ...  Section 3 introduces the recent developments for automating machine learning pipeline building and experimentation via automated machine learning (AutoML), where AutoML is a research area that focuses  ... 
doi:10.3390/info11040193 fatcat:hetp7ngcpbbcpkhdcyowuiiwxe

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence [article]

Sebastian Raschka, Joshua Patterson, Corey Nolet
2020 arXiv   pre-print
This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it.  ...  We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.  ...  Acknowledgments: We would like to thank John Zedlewski, Dante Gama Dessavre, and Thejaswi Nanditale from the RAPIDS team at NVIDIA and Scott Sievert for helpful feedback on the manuscript.  ... 
arXiv:2002.04803v2 fatcat:lvbczmz7xvbyjhs65zubwluzb4

autoBOT: evolving neuro-symbolic representations for explainable low resource text classification

Blaž Škrlj, Matej Martinc, Nada Lavrač, Senja Pollak
2021 Machine Learning  
We present autoBOT (automatic Bags-Of-Tokens), an autoML approach suitable for low resource learning scenarios, where both the hardware and the amount of data required for training are limited.  ...  This paper explores how automatically evolved text representations can serve as a basis for explainable, low-resource branch of models with competitive performance that are subject to automated hyperparameter  ...  We also gratefully acknowledge the support of NVIDIA Corporation for the donation of Titan-XP GPU.  ... 
doi:10.1007/s10994-021-05968-x pmid:34720391 pmcid:PMC8550026 fatcat:p7unysvjqraonlzta6ktlvfsui

Gandiva: Introspective Cluster Scheduling for Deep Learning

Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel, Xuan Peng, Hanyu Zhao, Quanlu Zhang, Fan Yang, Lidong Zhou
2018 USENIX Symposium on Operating Systems Design and Implementation  
Acknowledgments We thank our shepherd KyoungSoo Park and the anonymous reviewers for their valuable comments and suggestions.  ...  We thank Bin Wang and Shuguang Liu from Bing search platform team and Daniel Li, Subir Sidhu, and Chandu Thekkath from Microsoft AI Platform team for providing access to the GPU clusters and Azure GPU  ...  naïve implementation.  ... 
dblp:conf/osdi/XiaoBRSKHPPZZYZ18 fatcat:drtvebmtbvc6rkruvgssncmmtm

Effective data pre-processing for AutoML

Joseph Giovanelli, Besim Bilalli, Alberto Abelló
2021 International Workshop on Data Warehousing and OLAP  
Data pre-processing plays a key role in a data analytics process (e.g., supervised learning).  ...  Once found, these pipelines can be optimized using AutoML in order to generate executable pipelines (i.e., with parametrized operators for each transformation).  ...  We thank University of Bologna for issuing a grant for author's research stay at Universitat Politècnica de Catalunya. Finally, we thank Matteo Golfarelli for his comments and feedback on this work.  ... 
dblp:conf/dolap/GiovanelliBA21 fatcat:ly4eg32pvza6jgws5g6sjgxs5a

Design Patterns for Resource-Constrained Automated Deep-Learning Methods

Lukas Tuggener, Mohammadreza Amirian, Fernando Benites, Pius von Däniken, Prakhar Gupta, Frank-Peter Schilling, Thilo Stadelmann
2020 AI  
We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges  ...  search; we show (c) that in text processing deep-learning-based methods only pull ahead of traditional methods for short text lengths with less than a thousand characters under tight resource limitations  ...  Survey of Related Work Automated machine learning (AutoML) and by extension AutoDL is usually defined as the combined algorithm selection and hyperparameter optimization (CASH) problem [29] , which encompasses  ... 
doi:10.3390/ai1040031 fatcat:joz2b36rmnclhkbmijhlqrgznm

SmartValidator: A Framework for Automatic Identification and Classification of Cyber Threat Data [article]

Chadni Islam, M. Ali Babar, Roland Croft, Helge Janicke
2022 arXiv   pre-print
SmartValidator leverages Machine Learning (ML) techniques to enable automated validation of alerts.  ...  of the PoC for use in a real-world organization.  ...  Considering a SOC's security team capability, the model building process can also be replaced with Automated Machine Learning (AutoML) 12 framework such as Google Cloud AutoML 13 .  ... 
arXiv:2203.07603v1 fatcat:znk4aso2o5gk7fz2flf52ogi3q

Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility [article]

Konstantinos Kousias, Apostolos Pappas, Ozgu Alay, Antonios Argyriou, Michael Riegler
2020 arXiv   pre-print
We instrument HINDSIGHT++ following an Automated Machine Learning (AutoML) paradigm to first, alleviate the burden of data preprocessing, and second, enhance performance related aspects.  ...  Due to its universal design, we argue that HINDSIGHT++ can serve as a handy software tool for a multitude of applications in other scientific fields.  ...  Toward this goal, we adopt the concept of Automated Machine Learning (AutoML), an acronym used to describe the process of data pipeline automation.  ... 
arXiv:2011.10563v1 fatcat:3h3teaszyzc3lcokupyz3pptya

Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania

Johannes Benecke, Cornelius Benecke, Marius Ciutan, Mihnea Dosius, Cristian Vladescu, Victor Olsavszky, Kate Zinszer
2021 PLoS Neglected Tropical Diseases  
Furthermore, we performed predictions using a novel automated time series (AutoTS) machine learning tool and could interestingly show a stable course for these parasitic NTD.  ...  Through descriptive and inferential analysis, we observed a decline in case numbers for all three NTD. Several distributional particularities at regional level emerged.  ...  Acknowledgments We thank Paul Keil, Martin Kammerer and Robert Drews for excellent technical support. We are grateful to Victor S. Olsavszky for scientific discussions.  ... 
doi:10.1371/journal.pntd.0009831 pmid:34723982 pmcid:PMC8584970 fatcat:qmwplxcicjdzhbvnv7ww3q6hqy
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