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Sherpa: Robust Hyperparameter Optimization for Machine Learning

May 08, 2020
Lars Hertel, Julian Collado, Peter Sadowski, Jordan Ott, Pierre Baldi


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Learning in the Machine: the Symmetries of the Deep Learning Channel

Dec 22, 2017
Pierre Baldi, Peter Sadowski, Zhiqin Lu


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Learning in the Machine: Random Backpropagation and the Deep Learning Channel

Dec 22, 2017
Pierre Baldi, Peter Sadowski, Zhiqin Lu


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Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning

Jun 06, 2017
Peter Sadowski, Balint Radics, Ananya, Yasunori Yamazaki, Pierre Baldi


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Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

Mar 10, 2017
Chase Shimmin, Peter Sadowski, Pierre Baldi, Edison Weik, Daniel Whiteson, Edward Goul, Andreas Søgaard

* Phys. Rev. D 96, 074034 (2017) 

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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

Dec 06, 2016
Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat


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A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation

Oct 21, 2016
Pierre Baldi, Peter Sadowski

* Neural Networks, vol. 83, pp. 51-74, Nov. 2016 

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Theano: A Python framework for fast computation of mathematical expressions

May 09, 2016
The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

* 19 pages, 5 figures 

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Parameterized Machine Learning for High-Energy Physics

Jan 28, 2016
Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, Daniel Whiteson

* For submission to PRD 

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Learning Activation Functions to Improve Deep Neural Networks

Apr 21, 2015
Forest Agostinelli, Matthew Hoffman, Peter Sadowski, Pierre Baldi

* Accepted as a workshop paper contribution at the International Conference on Learning Representations (ICLR) 2015 

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Enhanced Higgs to $Ď„^+Ď„^-$ Searches with Deep Learning

Oct 13, 2014
Pierre Baldi, Peter Sadowski, Daniel Whiteson

* Phys. Rev. Lett. 114, 111801 (2015) 
* For submission to PRL 

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