Contents
- Interesting publications and tutorials on automated machine learning
- Interesting software packages that are not (yet) covered by any of my blog posts
Interesting publications and tutorials on automated machine learning
General
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Awad et al. (2020): Differential Evolution for Neural Architecture Search. arXiv:2012.06400
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Feurer et al. (2020): Auto-Sklearn 2.0: The Next Generation. arXiv:2007.04074
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Hu et al. (2020): Multi-objective Neural Architecture Search with Almost No Training. arXiv:2011.13591
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Kedziora et al. (2020): AutonoML: Towards an Integrated Framework for Autonomous Machine Learning. arXiv:2012.12600
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Xie et al. (2020): Skillearn: Machine Learning Inspired by Humans’ Learning Skills. arXiv:2012.04863
Distributed and decentralized machine learning
- Pramod (2018): Elastic Gossip: Distributing Neural Network Training Using Gossip-like Protocols. arXiv:1812.02407
Feature engineering
- James Max Kanter, Kalyan Veeramachaneni. Deep feature synthesis: Towards automating data science endeavors. IEEE DSAA 2015. available online: https://dai.lids.mit.edu/wp-content/uploads/2017/10/DSAA_DSM_2015.pdf.
Meta learning
- Shaw et al. (2018): Bayesian Meta-network Architecture Learning. arXiv:1812.09584
Neural architecture search
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Anderson et al. (2020): Performance-Oriented Neural Architecture Search. arXiv:2001.02976
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Bulat et al. (2020): BATS: Binary ArchitecTure Search. arXiv:2003.01711
- Cai et al. (2018): ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. arXiv:1812.00332
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Chen et al. (2019): Binarized Neural Architecture Search. arXiv:1911.10862
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de Laroussilhe et al. (2018): Neural Architecture Search Over a Graph Search Space. arXiv:1812.10666
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Geifman and El-Yaniv (2018): Deep Active Learning with a Neural Architecture Search. arXiv:1811.07579
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Huang and Chu (2020): PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment. arXiv:2003.05112
- Lindauer and Hutter (2020): Best Practices for Scientific Research on Neural Architecture Search. JMLR 21(243): 1-18
- Liu et al. (2017): Progressive Neural Architecture Search. arXiv:1712.00559
- Liu et al. (2018): DARTS: Differentiable Architecture Search. arXiv:1806.09055
- Liu et al. (2020): Are Labels Necessary for Neural Architecture Search? arXiv:2003.12056
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Luo et al. (2020): Semi-Supervised Neural Architecture Search. arXiv:2002.10389
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Tan et al. (2018): MnasNet: Platform-Aware Neural Architecture Search for Mobile. arXiv:1807.11626
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van Wyk and Bosman (2018): Evolutionary Neural Architecture Search for Image Restoration. arXiv:1812.05866
- White et al. (2019): BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search. arXiv:1910.11858
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Wu et al. (2018): Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search. arXiv:1812.00090
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Xie et al. (2018): SNAS: Stochastic Neural Architecture Search. arXiv:1812.09926
- Zela et al. (2020): NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search. arXiv:2001.10422
- Zhang et al. (2019): Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising. arXiv:1909.08228
Misc autoML and optimization related
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Drori et al. (2019): Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar. arXiv:1905.10345
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Jones et al. (1998): Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization 13, 455-492. doi:10.1023/A:1008306431147