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Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks

Oct 20, 2020
Michael James Fenton, Alexander Shmakov, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

* 8pages 

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Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning

Jul 30, 2020
Lars Hertel, Pierre Baldi, Daniel L. Gillen


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SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness

Jun 16, 2020
Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi


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Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

Jun 15, 2020
Stephen McAleer, John Lanier, Roy Fox, Pierre Baldi

* SM and JL contributed equally 

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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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Continuous Representation of Molecules Using Graph Variational Autoencoder

Apr 17, 2020
Mohammadamin Tavakoli, Pierre Baldi


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A Fortran-Keras Deep Learning Bridge for Scientific Computing

Apr 14, 2020
Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi


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Learning in the Machine: To Share or Not to Share?

Oct 04, 2019
Jordan Ott, Erik Linstead, Nicholas LaHaye, Pierre Baldi


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Curiosity-Driven Multi-Criteria Hindsight Experience Replay

Jun 09, 2019
John B. Lanier, Stephen McAleer, Pierre Baldi

* 14 pages 

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The capacity of feedforward neural networks

Jan 02, 2019
Pierre Baldi, Roman Vershynin

* 46 pages 

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Efficient Neutrino Oscillation Parameter Inference with Gaussian Process

Nov 16, 2018
Lingge Li, Nitish Nayak, Jianming Bian, Pierre Baldi


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Solving the Rubik's Cube Without Human Knowledge

May 18, 2018
Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi

* First three authors contributed equally. Submitted to NIPS 2018 

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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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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 $τ^+τ^-$ 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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Complex-Valued Autoencoders

Mar 18, 2014
Pierre Baldi, Zhiqin Lu

* Neural Networks, 33, (2012), 136-147 
* Final version, journal ref added 

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