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Thomas Elsken
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2020 – today
- 2023
- [j5]Rohit Mohan, Thomas Elsken, Arber Zela, Jan Hendrik Metzen, Benedikt Staffler, Thomas Brox, Abhinav Valada, Frank Hutter:
Neural Architecture Search for Dense Prediction Tasks in Computer Vision. Int. J. Comput. Vis. 131(7): 1784-1807 (2023) - [c6]Gresa Shala, Thomas Elsken, Frank Hutter, Josif Grabocka:
Transfer NAS with Meta-learned Bayesian Surrogates. ICLR 2023 - [i11]Colin White, Mahmoud Safari, Rhea Sukthanker, Binxin Ru, Thomas Elsken, Arber Zela, Debadeepta Dey, Frank Hutter:
Neural Architecture Search: Insights from 1000 Papers. CoRR abs/2301.08727 (2023) - 2022
- [i10]Thomas Elsken, Arber Zela, Jan Hendrik Metzen, Benedikt Staffler, Thomas Brox, Abhinav Valada, Frank Hutter:
Neural Architecture Search for Dense Prediction Tasks in Computer Vision. CoRR abs/2202.07242 (2022) - 2021
- [b1]Thomas Elsken:
Efficient and practical neural architecture search. University of Freiburg, Freiburg im Breisgau, Germany, 2021 - [c5]Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh:
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. NeurIPS 2021: 7898-7911 - [i9]Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo, Guilherme Miotto, Simon Schrodi, Andre Biedenkapp
, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter:
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. CoRR abs/2105.01015 (2021) - [i8]Thomas Elsken, Benedikt Staffler, Arber Zela, Jan Hendrik Metzen, Frank Hutter:
Bag of Tricks for Neural Architecture Search. CoRR abs/2107.03719 (2021) - 2020
- [j4]Christoph Schorn, Thomas Elsken, Sebastian Vogel, Armin Runge, Andre Guntoro
, Gerd Ascheid:
Automated design of error-resilient and hardware-efficient deep neural networks. Neural Comput. Appl. 32(24): 18327-18345 (2020) - [c4]Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter:
Meta-Learning of Neural Architectures for Few-Shot Learning. CVPR 2020: 12362-12372 - [c3]Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter:
Understanding and Robustifying Differentiable Architecture Search. ICLR 2020 - [i7]Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh:
Neural Ensemble Search for Performant and Calibrated Predictions. CoRR abs/2006.08573 (2020)
2010 – 2019
- 2019
- [j3]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Neural Architecture Search: A Survey. J. Mach. Learn. Res. 20: 55:1-55:21 (2019) - [c2]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution. ICLR (Poster) 2019 - [p1]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Neural Architecture Search. Automated Machine Learning 2019: 63-77 - [i6]Arber Zela
, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter:
Understanding and Robustifying Differentiable Architecture Search. CoRR abs/1909.09656 (2019) - [i5]Christoph Schorn, Thomas Elsken, Sebastian Vogel, Armin Runge, Andre Guntoro, Gerd Ascheid:
Automated design of error-resilient and hardware-efficient deep neural networks. CoRR abs/1909.13844 (2019) - [i4]Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter:
Meta-Learning of Neural Architectures for Few-Shot Learning. CoRR abs/1911.11090 (2019) - 2018
- [c1]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Simple and efficient architecture search for Convolutional Neural Networks. ICLR (Workshop) 2018 - [i3]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Multi-objective Architecture Search for CNNs. CoRR abs/1804.09081 (2018) - [i2]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Neural Architecture Search: A Survey. CoRR abs/1808.05377 (2018) - 2017
- [i1]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Simple And Efficient Architecture Search for Convolutional Neural Networks. CoRR abs/1711.04528 (2017)
1990 – 1999
- 1999
- [j2]Thomas Elsken:
Smaller nets may perform better: special transfer functions. Neural Networks 12(4-5): 627-645 (1999) - 1997
- [j1]Thomas Elsken:
Even on Finite Test Sets Smaller Nets may Perform Better. Neural Networks 10(2): 369-385 (1997)
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