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Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics


Sep 17, 2020
Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin D. Cubuk, Patrick Riley, Kieron Burke


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Scaling Symbolic Methods using Gradients for Neural Model Explanation


Jun 29, 2020
Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley


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Machine learning on DNA-encoded libraries: A new paradigm for hit-finding


Jan 31, 2020
Kevin McCloskey, Eric A. Sigel, Steven Kearnes, Ling Xue, Xia Tian, Dennis Moccia, Diana Gikunju, Sana Bazzaz, Betty Chan, Matthew A. Clark, John W. Cuozzo, Marie-Aude Guié, John P. Guilinger, Christelle Huguet, Christopher D. Hupp, Anthony D. Keefe, Christopher J. Mulhern, Ying Zhang, Patrick Riley


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Decoding Molecular Graph Embeddings with Reinforcement Learning


Apr 18, 2019
Steven Kearnes, Li Li, Patrick Riley

* Preliminary work. Under review at the ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data. Copyright 2019 by the author(s) 

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Neural-Guided Symbolic Regression with Semantic Prior


Jan 23, 2019
Li Li, Minjie Fan, Rishabh Singh, Patrick Riley


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Optimization of Molecules via Deep Reinforcement Learning


Oct 23, 2018
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, Patrick Riley

* Adds Supporting Information 

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Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds


May 18, 2018
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, Patrick Riley

* changes for NIPS submission 

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Molecular Graph Convolutions: Moving Beyond Fingerprints


Aug 18, 2016
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley

* J Comput Aided Mol Des (2016) 
* See "Version information" section 

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Massively Multitask Networks for Drug Discovery


Feb 06, 2015
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande

* Preliminary work. Under review by the International Conference on Machine Learning (ICML) 

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