T. Nathan Mundhenk

29 papers A* 4A 2Misc 1Journal 11Unranked 11

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics. CoRR Conor F. Hayes, Felipe Leno da Silva, Jiachen Yang, T. Nathan Mundhenk, Chak Shing Lee, Jacob F. Pettit, Claudio Santiago, Sookyung Kim, Joanne Taery Kim, Ignacio Aravena Solis, Ruben Glatt, Andre R. Goncalves, Alexander Ladd, Ahmet Can Solak, Thomas Desautels, Daniel M. Faissol, Brenden K. Petersen, Mikel Landajuela
2025	J	`jnl`	SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. IEEE Trans. Evol. Comput. 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, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, Hengzhe Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
2023	J	`jnl`	Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR 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, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
2021	A*	`conf`	Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. ICLR Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk, Cláudio Prata Santiago, Sookyung Kim, Joanne Taery Kim
2021	A*	`conf`	Discovering symbolic policies with deep reinforcement learning. ICML Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol
2021	J	`jnl`	Explaining neural network predictions of material strength. CoRR Ian A. Palmer, T. Nathan Mundhenk, Brian Gallagher, Yong Han
2021	J	`jnl`	Improving exploration in policy gradient search: Application to symbolic optimization. CoRR Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol
2021	A*	`conf`	Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. NeurIPS T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen
2021	J	`jnl`	Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. CoRR T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen
2020	—	`conf`	On the Impact of Perceptual Compression on Deep Learning. MIPR Gerald Friedland, Ruoxi Jia, Jingkang Wang, Bo Li, T. Nathan Mundhenk
2019	J	`jnl`	Efficient Saliency Maps for Explainable AI. CoRR T. Nathan Mundhenk, Barry Y. Chen, Gerald Friedland
2018	A*	`conf`	Improvements to Context Based Self-Supervised Learning. CVPR T. Nathan Mundhenk, Daniel Ho, Barry Y. Chen
2017	A	`conf`	Deep Multi-modal Vehicle Detection in Aerial ISR Imagery. WACV Wesam A. Sakla, Goran Konjevod, T. Nathan Mundhenk
2017	—	`conf`	Deep learning for evaluating difficult-to-detect incomplete repairs of high fluence laser optics at the National Ignition Facility. QCAV T. Nathan Mundhenk, Laura M. Kegelmeyer, Scott K. Trummer
2017	J	`jnl`	Improvements to context based self-supervised learning. CoRR T. Nathan Mundhenk, Daniel Ho, Barry Y. Chen
2016	—	`conf`	A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning. ECCV (3) T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla, Kofi Boakye
2016	J	`jnl`	A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning. CoRR T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla, Kofi Boakye
2016	A	`conf`	Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications. SC 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
2015	—	`conf`	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 T. Nathan Mundhenk, Kyungnam Kim, Yuri Owechko
2014	—	`conf`	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 T. Nathan Mundhenk, Arturo Flores, Heiko Hoffman
2014	—	`conf`	Frame Rate Fusion and Upsampling of EO/LIDAR Data for Multiple Platforms. CVPR Workshops T. Nathan Mundhenk, Kyungnam Kim, Yuri Owechko
2013	Misc	`conf`	Using tactile displays to maintain situational awareness during driving. CogSIMA Mike Howard, Rashmi Sundareswara, Mike Daily, Rajan Bhattacharyya, Sam Kaplan, T. Nathan Mundhenk, Craig Lee, Howard Neely
2012	—	`conf`	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 T. Nathan Mundhenk, Kang-Yu Ni, Yang Chen, Kyungnam Kim, Yuri Owechko
2011	—	`conf`	High precision object segmentation and tracking for use in super resolution video reconstruction. Intelligent Robots and Computer Vision: Algorithms and Techniques T. Nathan Mundhenk, Rashmi Sundareswara, David R. Gerwe, Yang Chen
2005	J	`jnl`	Computational modeling and exploration of contour integration for visual saliency. Biol. Cybern. T. Nathan Mundhenk, Laurent Itti
2004	—	`conf`	Biologically inspired feature-based categorization of objects. Human Vision and Electronic Imaging T. Nathan Mundhenk, Vidhya Navalpakkam, Hendrik Makaliwe, Shrihari Vasudevan, Laurent Itti
2003	J	`jnl`	CINNIC, a new computational algorithm for the modeling of early visual contour integration in humans. Neurocomputing T. Nathan Mundhenk, Laurent Itti
2002	—	`conf`	A Model of Contour Integration in Early Visual Cortex. Biologically Motivated Computer Vision T. Nathan Mundhenk, Laurent Itti
2002	—	`conf`	A New Robotics Platform for Neuromorphic Vision: Beobots. Biologically Motivated Computer Vision Daesu Chung, Reid Hirata, T. Nathan Mundhenk, Jen Ng, Robert J. Peters, Eric Pichon, April Tsui, Tong Ventrice, Dirk B. Walther, Philip Williams, Laurent Itti