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Kevin Swersky
Ph.D
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I try to maintain a broad set of interests within machine learning, although I specialize in deep learning. Most recently, I have become interested in Bayesian optimization for automatically tuning the meta-parameters of neural networks (and other machine learning models). This approach has already had tremendous success on several benchmark datasets and often out-performs experts in tuning these models. I think this is a promising avenue for making deep learning more accessible, both within the machine learning community and beyond.
Papers52 papers
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ICLR, (2020)
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen,David Duvenaud,Mohammad Norouzi,Kevin Swersky
ICLR, (2020)
NIPS 2020, (2020)
ICML, pp.6987-6998, (2020)
ICML, pp.6237-6247, (2020)
Eleni Triantafillou,Tyler Zhu,Vincent Dumoulin, Pascal Lamblin, Utku Evci,Kelvin Xu, Ross Goroshin, Carles Gelada,Kevin Swersky,Pierre-Antoine Manzagol,Hugo Larochelle
ICLR, (2020)
international conference on learning representations, (2020)
Elliot Creager,David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis,Kevin Swersky,Toniann Pitassi,Richard S. Zemel
international conference on machine learning, (2019)
CoRR, (2019)
Eleni Triantafillou,Tyler Zhu,Vincent Dumoulin,Pascal Lamblin,Kelvin Xu, Ross Goroshin, Carles Gelada,Kevin Swersky,Pierre-Antoine Manzagol,Hugo Larochelle
arXiv: Learning, (2019)
CoRR, (2019)
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 13556-13566
international conference on acoustics, speech, and signal processing, pp.5799-5803, (2018)
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ICASSP, (2018)
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