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ORCIDT. Nathan Mundhenk
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2020 – today
- 2023
[i7]Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer
, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden K. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava:
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023)- 2022
- [c20]Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Cláudio P. Santiago, Ignacio Aravena, Terrell Nathan Mundhenk, Garrett Mulcahy, Brenden K. Petersen:
A Unified Framework for Deep Symbolic Regression. NeurIPS 2022 - 2021
- [c19]Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk, Cláudio Prata Santiago, Sookyung Kim, Joanne Taery Kim:
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. ICLR 2021 - [c18]Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Discovering symbolic policies with deep reinforcement learning. ICML 2021: 5979-5989 - [c17]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. NeurIPS 2021: 24912-24923 - [i6]Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Improving exploration in policy gradient search: Application to symbolic optimization. CoRR abs/2107.09158 (2021) - [i5]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. CoRR abs/2111.00053 (2021) - [i4]Ian A. Palmer, T. Nathan Mundhenk, Brian Gallagher, Yong Han:
Explaining neural network predictions of material strength. CoRR abs/2111.03729 (2021) - 2020
- [c16]Gerald Friedland, Ruoxi Jia, Jingkang Wang, Bo Li, T. Nathan Mundhenk:
On the Impact of Perceptual Compression on Deep Learning. MIPR 2020: 219-224
2010 – 2019
- 2019
- [i3]T. Nathan Mundhenk, Barry Y. Chen, Gerald Friedland:
Efficient Saliency Maps for Explainable AI. CoRR abs/1911.11293 (2019) - 2018
- [c15]T. Nathan Mundhenk, Daniel Ho, Barry Y. Chen:
Improvements to Context Based Self-Supervised Learning. CVPR 2018: 9339-9348 - 2017
- [c14]T. Nathan Mundhenk, Laura M. Kegelmeyer, Scott K. Trummer:
Deep learning for evaluating difficult-to-detect incomplete repairs of high fluence laser optics at the National Ignition Facility. QCAV 2017: 103380H - [c13]Wesam A. Sakla, Goran Konjevod, T. Nathan Mundhenk:
Deep Multi-modal Vehicle Detection in Aerial ISR Imagery. WACV 2017: 916-923 - [i2]T. Nathan Mundhenk, Daniel Ho, Barry Y. Chen:
Improvements to context based self-supervised learning. CoRR abs/1711.06379 (2017) - 2016
- [c12]T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla, Kofi Boakye:
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning. ECCV (3) 2016: 785-800 - [c11]Jun Sawada, Filipp Akopyan, Andrew S. Cassidy, Brian Taba, Michael V. DeBole, Pallab Datta, Rodrigo Alvarez-Icaza, Arnon Amir, John V. Arthur, Alexander Andreopoulos, Rathinakumar Appuswamy, Heinz Baier, Davis Barch, David J. Berg, Carmelo di Nolfo, Steven K. Esser, Myron Flickner, Thomas A. Horvath, Bryan L. Jackson, Jeff Kusnitz, Scott Lekuch, Michael Mastro, Timothy Melano, Paul A. Merolla, Steven E. Millman, Tapan K. Nayak, Norm Pass, Hartmut E. Penner, William P. Risk, Kai Schleupen, Benjamin G. Shaw, Hayley Wu, Brian Giera, Adam T. Moody, T. Nathan Mundhenk, Brian Van Essen, Eric X. Wang, David P. Widemann, Qing Wu, William E. Murphy, Jamie K. Infantolino, James A. Ross, Dale R. Shires, Manuel M. Vindiola, Raju Namburu, Dharmendra S. Modha:
Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications. SC 2016: 130-141 - [i1]T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla, Kofi Boakye:
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning. CoRR abs/1609.04453 (2016) - 2015
- [c10]T. Nathan Mundhenk, Kyungnam Kim, Yuri Owechko:
PanDAR: a wide-area, frame-rate, and full color lidar with foveated region using backfilling interpolation upsampling. Intelligent Robots and Computer Vision: Algorithms and Techniques 2015: 94060K - 2014
- [c9]T. Nathan Mundhenk, Kyungnam Kim, Yuri Owechko:
Frame Rate Fusion and Upsampling of EO/LIDAR Data for Multiple Platforms. CVPR Workshops 2014: 762-769 - [c8]T. Nathan Mundhenk, Arturo Flores, Heiko Hoffman:
Classification and segmentation of orbital space based objects against terrestrial distractors for the purpose of finding holes in shape from motion 3D reconstruction. Intelligent Robots and Computer Vision: Algorithms and Techniques 2014: 90250K - 2013
- [c7]Mike Howard, Rashmi Sundareswara, Mike Daily, Rajan Bhattacharyya, Sam Kaplan, T. Nathan Mundhenk, Craig Lee, Howard Neely:
Using tactile displays to maintain situational awareness during driving. CogSIMA 2013: 228-237 - 2012
- [c6]Kang-Yu Ni, Terrell Nathan Mundhenk, Kyungnam Kim, Yuri Owechko:
Manifold-based fingerprinting for target identification. CVPR Workshops 2012: 1-6 - [c5]T. Nathan Mundhenk, Kang-Yu Ni, Yang Chen, Kyungnam Kim, Yuri Owechko:
Detection of unknown targets from aerial camera and extraction of simple object fingerprints for the purpose of target reacquisition. Intelligent Robots and Computer Vision: Algorithms and Techniques 2012: 83010H - 2011
- [c4]T. Nathan Mundhenk, Rashmi Sundareswara, David R. Gerwe, Yang Chen:
High precision object segmentation and tracking for use in super resolution video reconstruction. Intelligent Robots and Computer Vision: Algorithms and Techniques 2011: 78780G
2000 – 2009
- 2005
- [j2]T. Nathan Mundhenk, Laurent Itti:
Computational modeling and exploration of contour integration for visual saliency. Biol. Cybern. 93(3): 188-212 (2005) - 2004
- [c3]T. Nathan Mundhenk, Vidhya Navalpakkam, Hendrik Makaliwe, Shrihari Vasudevan, Laurent Itti:
Biologically inspired feature-based categorization of objects. Human Vision and Electronic Imaging 2004: 330-341 - 2003
- [j1]T. Nathan Mundhenk, Laurent Itti:
CINNIC, a new computational algorithm for the modeling of early visual contour integration in humans. Neurocomputing 52-54: 599-604 (2003) - 2002
- [c2]T. Nathan Mundhenk, Laurent Itti:
A Model of Contour Integration in Early Visual Cortex. Biologically Motivated Computer Vision 2002: 80-89 - [c1]Daesu Chung, Reid Hirata, T. Nathan Mundhenk, Jen Ng, Robert J. Peters, Eric Pichon, April Tsui, Tong Ventrice, Dirk Walther, Philip Williams, Laurent Itti:
A New Robotics Platform for Neuromorphic Vision: Beobots. Biologically Motivated Computer Vision 2002: 558-566
Coauthor Index
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