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Symbolic Regression via Neural-Guided Genetic Programming Population Seeding


Nov 17, 2021
T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Claudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen

* Accepted at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021) 

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Explaining neural network predictions of material strength


Nov 05, 2021
Ian A. Palmer, T. Nathan Mundhenk, Brian Gallagher, Yong Han


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Improving exploration in policy gradient search: Application to symbolic optimization


Jul 19, 2021
Mikel Landajuela Larma, Brenden K. Petersen, Soo K. Kim, Claudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol

* 1st Mathematical Reasoning in General Artificial Intelligence Workshop, ICLR 2021 
* Published in 1st Mathematical Reasoning in General Artificial Intelligence Workshop, ICLR 2021 

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Efficient Saliency Maps for Explainable AI


Nov 26, 2019
T. Nathan Mundhenk, Barry Y. Chen, Gerald Friedland

* In submission to ICLR 2020 

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Improvements to context based self-supervised learning


Mar 28, 2018
T. Nathan Mundhenk, Daniel Ho, Barry Y. Chen

* Accepted paper at CVPR 2018 

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A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning


Sep 14, 2016
T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla, Kofi Boakye

* ECCV 2016 Pre-press revision 

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