T. Nathan Mundhenk

Orcid: 0000-0002-3508-0341

According to our database1, T. Nathan Mundhenk authored at least 27 papers between 2002 and 2023.

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Bibliography

2023
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition.
CoRR, 2023

2022
A Unified Framework for Deep Symbolic Regression.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Explaining neural network predictions of material strength.
CoRR, 2021

Symbolic Regression via Neural-Guided Genetic Programming Population Seeding.
CoRR, 2021

Improving exploration in policy gradient search: Application to symbolic optimization.
CoRR, 2021

Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Discovering symbolic policies with deep reinforcement learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
On the Impact of Perceptual Compression on Deep Learning.
Proceedings of the 3rd IEEE Conference on Multimedia Information Processing and Retrieval, 2020

2019
Efficient Saliency Maps for Explainable AI.
CoRR, 2019

2018
Improvements to Context Based Self-Supervised Learning.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017
Deep Multi-modal Vehicle Detection in Aerial ISR Imagery.
Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision, 2017

Deep learning for evaluating difficult-to-detect incomplete repairs of high fluence laser optics at the National Ignition Facility.
Proceedings of the Thirteenth International Conference on Quality Control by Artificial Vision, 2017

2016

A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning.
Proceedings of the Computer Vision - ECCV 2016, 2016

2015
PanDAR: a wide-area, frame-rate, and full color lidar with foveated region using backfilling interpolation upsampling.
Proceedings of the Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 2015

2014
Classification and segmentation of orbital space based objects against terrestrial distractors for the purpose of finding holes in shape from motion 3D reconstruction.
Proceedings of the Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques, 2014

Frame Rate Fusion and Upsampling of EO/LIDAR Data for Multiple Platforms.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013
Using tactile displays to maintain situational awareness during driving.
Proceedings of the IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, 2013

2012
Detection of unknown targets from aerial camera and extraction of simple object fingerprints for the purpose of target reacquisition.
Proceedings of the Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, 2012

Manifold-based fingerprinting for target identification.
Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012

2011
High precision object segmentation and tracking for use in super resolution video reconstruction.
Proceedings of the Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, 2011

2005
Computational modeling and exploration of contour integration for visual saliency.
Biol. Cybern., 2005

2004
Biologically inspired feature-based categorization of objects.
Proceedings of the Human Vision and Electronic Imaging IX, 2004

2003
CINNIC, a new computational algorithm for the modeling of early visual contour integration in humans.
Neurocomputing, 2003

2002
A Model of Contour Integration in Early Visual Cortex.
Proceedings of the Biologically Motivated Computer Vision Second International Workshop, 2002

A New Robotics Platform for Neuromorphic Vision: Beobots.
Proceedings of the Biologically Motivated Computer Vision Second International Workshop, 2002


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