Terrell Mundhenk
Terrell Mundhenk
Machine Learning Group Leader, Lawrence Livermore National Lab
Verified email at - Homepage
Cited by
Cited by
A large contextual dataset for classification, detection and counting of cars with deep learning
TN Mundhenk, G Konjevod, WA Sakla, K Boakye
Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
BK Petersen, M Landajuela, TN Mundhenk, CP Santiago, SK Kim, JT Kim
arXiv preprint arXiv:1912.04871, 2019
Schizophrenia and the mirror system: an essay
MA Arbib, TN Mundhenk
Neuropsychologia 43 (2), 268-280, 2005
Improvements to context based self-supervised learning
TN Mundhenk, D Ho, BY Chen
Proceedings of the IEEE conference on computer vision and pattern …, 2018
Symbolic regression via deep reinforcement learning enhanced genetic programming seeding
T Mundhenk, M Landajuela, R Glatt, CP Santiago, BK Petersen
Advances in Neural Information Processing Systems 34, 24912-24923, 2021
Discovering symbolic policies with deep reinforcement learning
DF Mikel Landajuela, Brenden K Petersen, Sookyung Kim, Claudio P Santiago ...
International Conference on Machine Learning, 5979-5989, 2021
Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications
J Sawada, F Akopyan, AS Cassidy, B Taba, MV Debole, P Datta, ...
SC'16: Proceedings of the International Conference for High Performance …, 2016
Efficient saliency maps for explainable AI
TN Mundhenk, BY Chen, G Friedland
arXiv preprint arXiv:1911.11293, 2019
A bottom–up model of spatial attention predicts human error patterns in rapid scene recognition
W Einhäuser, TN Mundhenk, P Baldi, C Koch, L Itti
Journal of Vision 7 (10), 6-6, 2007
System and method to improve object tracking using tracking fingerprints
K Kim, C Jeong, TN Mundhenk, Y Owechko
US Patent 9,940,726, 2018
Techniques for fisheye lens calibration using a minimal number of measurements
TN Mundhenk, MJ Rivett, X Liao, EL Hall
Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and …, 2000
Deep multi-modal vehicle detection in aerial ISR imagery
W Sakla, G Konjevod, TN Mundhenk
2017 IEEE winter conference on applications of computer vision (WACV), 916-923, 2017
Computational modeling and exploration of contour integration for visual saliency
TN Mundhenk, L Itti
Biological cybernetics 93 (3), 188-212, 2005
System for identifying regions of interest in visual imagery
D Khosla, R Bhattacharyya, TN Mundhenk, DJ Huber
US Patent 8,774,517, 2014
Predicting compressive strength of consolidated molecular solids using computer vision and deep learning
B Gallagher, M Rever, D Loveland, TN Mundhenk, B Beauchamp, ...
Materials & Design 190, 108541, 2020
Backfilling clouds of 3D coordinates
TN Mundhenk, Y Owechko, K Kim
US Patent 9,772,405, 2017
A unified framework for deep symbolic regression
M Landajuela, CS Lee, J Yang, R Glatt, CP Santiago, I Aravena, ...
Advances in Neural Information Processing Systems 35, 33985-33998, 2022
Backfilling points in a point cloud
TN Mundhenk, Y Owechko, K Kim
US Patent 9,811,880, 2017
A new robotics platform for neuromorphic vision: Beobots
D Chung, R Hirata, TN Mundhenk, J Ng, RJ Peters, E Pichon, A Tsui, ...
Biologically Motivated Computer Vision: Second International Workshop, BMCV …, 2002
Stereo-motion method of three-dimensional (3-D) structure information extraction from a video for fusion with 3-D point cloud data
TN Mundhenk, HW Chen, Y Owechko, D Korchev, K Kim, Z Zhang
US Patent 9,761,002, 2017
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