Automated Machine Learning in Practice: State of the Art and Recent Results

19 July 2019

Papers citing "Automated Machine Learning in Practice: State of the Art and Recent Results"

8 / 8 papers shown

Title
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods A. Mumuni F. Mumuni 70 5 0 13 Mar 2024
AutoML in The Wild: Obstacles, Workarounds, and Expectations Yuan Sun Qiurong Song Xinning Gui Fenglong Ma Ting Wang 21 13 0 21 Feb 2023
PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis Mohammadreza Amirian Javier A. Montoya-Zegarra Jonathan Gruss Yves D. Stebler A. S. Bozkir M. Calandri Friedhelm Schwenker Thilo Stadelmann MedIm 13 3 0 19 Aug 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 38 78 0 06 May 2022
Review of automated time series forecasting pipelines Stefan Meisenbacher Marian Turowski Kaleb Phipps Martin Ratz D. Muller V. Hagenmeyer Ralf Mikut TPM AI4TS 22 52 0 03 Feb 2022
Fractional ridge regression: a fast, interpretable reparameterization of ridge regression Ariel S. Rokem Kendrick Norris Kay 6 42 0 07 May 2020
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 271 5,329 0 05 Nov 2016
Automating biomedical data science through tree-based pipeline optimization Randal S. Olson Ryan J. Urbanowicz Peter C. Andrews Nicole A. Lavender L. C. Kidd J. Moore AI4CE 33 311 0 28 Jan 2016