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ORCIDJosh Harguess
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2020 – today
- 2024
[i6]Chris M. Ward, Josh Harguess, Julia Tao, Daniel Christman, Paul Spicer, Mike Tan:
The AI Security Pyramid of Pain. CoRR abs/2402.11082 (2024)- 2022
- [c25]Ilyas Bankole-Hameed, Arav Parikh, Josh Harguess:
Exploring the Effect of Adversarial Attacks on Deep Learning Architectures for X-Ray Data. AIPR 2022: 1-9 - [c24]Josh Harguess, Chris M. Ward:
Is the Next Winter Coming for AI? Elements of Making Secure and Robust AI. AIPR 2022: 1-7 - [c23]Mohammad Zarei, Chris M. Ward, Josh Harguess, Marshal Aiken:
Adversarial Barrel! An Evaluation of 3D Physical Adversarial Attacks. AIPR 2022: 1-6 - 2021
- [c22]Shiri Bendelac, Keith Manville, Josh Harguess
, Mikel Rodriguez:
A Dynamic Thermal IR Display for Physical Adversarial Attacks. AIPR 2021: 1-7 - [c21]Emily Holt, Anto Malkastian, Savanna Smith, Chris M. Ward, Josh Harguess:
Baseline Evaluation Methodology for Adversarial Patterns on Object Detection Models. AIPR 2021: 1-6 - [c20]Michael Threet, Colin Busho, Josh Harguess, Melanie Jutras, Nicole Lape, Sara Leary, Keith Manville, Mike Tan, Chris M. Ward:
Physical Adversarial Attacks in Simulated Environments. AIPR 2021: 1-5 - [i5]Marissa Dotter, Sherry Xie, Keith Manville, Josh Harguess, Colin Busho, Mikel Rodriguez:
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks. CoRR abs/2101.02899 (2021)
2010 – 2019
- 2019
- [i4]Chris M. Ward, Josh Harguess, Cameron Hilton:
Ship classification from overhead imagery using synthetic data and domain adaptation. CoRR abs/1905.03894 (2019) - [i3]Chris M. Ward, Josh Harguess, Alexander G. Corelli:
Leveraging synthetic imagery for collision-at-sea avoidance. CoRR abs/1905.04828 (2019) - [i2]Chris M. Ward, Josh Harguess, Brendan Crabb, Shibin Parameswaran:
Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN). CoRR abs/1905.05373 (2019) - 2018
- [j2]Katie Rainey, Josh Harguess:
Report on the Second Annual Workshop on Naval Applications of Machine Learning. AI Mag. 39(4): 69-72 (2018) - [c19]Nancy Ronquillo, Josh Harguess:
On Evaluating Video-based Generative Adversarial Networks (GANs). AIPR 2018: 1-7 - 2017
- [j1]Pedro A. Forero, Scott Shafer, Josh Harguess:
Sparsity-Driven Laplacian-Regularized Outlier Identification for Dictionary Learning. IEEE Trans. Signal Process. 65(14): 3803-3817 (2017) - [c18]Nancy Ronquillo, Josh Harguess:
Informativeness of Degraded Data in Training a Classification System. AIPR 2017: 1-6 - 2016
- [c17]Josh Harguess, Chris Barngrover, Michael Reese:
Optical flow on degraded imagery. AIPR 2016: 1-6 - [c16]Andrew R. Kalukin, Andrew J. Maltenfort, John Irvine, Joshua Harguess:
Automated mensuration of in-scene targets for video quality assessment. AIPR 2016: 1-7 - 2015
- [c15]Pedro A. Forero, Scott Shafer, Josh Harguess:
Structured outlier models for robust dictionary learning. CISS 2015: 1-6 - [c14]Phillip Verbancsics, Josh Harguess:
Classifying Maritime Vessels from Satellite Imagery with HyperNEAT. GECCO (Companion) 2015: 1507-1508 - [c13]Phillip Verbancsics, Josh Harguess:
Feature Learning HyperNEAT: Evolving Neural Networks to Extract Features for Classification of Maritime Satellite Imagery. IPCAT 2015: 208-220 - [c12]Phillip Verbancsics, Josh Harguess:
Image Classification Using Generative Neuro Evolution for Deep Learning. WACV 2015: 488-493 - 2014
- [c11]Brian Okorn, Josh Harguess:
Ego-Motion Estimation on Range Images Using High-Order Polynomial Expansion. CVPR Workshops 2014: 299-306 - [c10]Phillip Verbancsics, Joshua Harguess:
Deep learning through generative and developmental system. GECCO (Companion) 2014: 103-104 - 2013
- [i1]Phillip Verbancsics, Josh Harguess:
Generative NeuroEvolution for Deep Learning. CoRR abs/1312.5355 (2013) - 2011
- [c9]Josh Harguess, Katie Rainey:
Are face recognition methods useful for classifying ships? AIPR 2011: 1-7 - [c8]Joshua Harguess, Changbo Hu, Jake K. Aggarwal:
Occlusion robust multi-camera face tracking. CVPR Workshops 2011: 31-38 - [c7]Joshua Harguess, Jake K. Aggarwal:
Is there a connection between face symmetry and face recognition? CVPR Workshops 2011: 66-73 - [c6]Josh Harguess, Changbo Hu, J. K. Aggarwal:
Full-motion recovery from multiple video cameras applied to face tracking and recognition. ICCV Workshops 2011: 1889-1896
2000 – 2009
- 2009
- [c5]Joshua Harguess, Jake K. Aggarwal:
A case for the average-half-face in 2D and 3D for face recognition. CVPR Workshops 2009: 7-12 - [c4]Changbo Hu, Josh Harguess, J. K. Aggarwal:
Patch-based face recognition from video. ICIP 2009: 3321-3324 - [c3]Josh Harguess, J. K. Aggarwal:
Semantic labeling of track events using time series segmentation and shape analysis. ICIP 2009: 4317-4320 - [c2]Josh Harguess, Changbo Hu, J. K. Aggarwal:
Fusing face recognition from multiple cameras. WACV 2009: 1-7 - 2008
- [c1]Josh Harguess, Shalini Gupta, Jake K. Aggarwal:
3D face recognition with the average-half-face. ICPR 2008: 1-4
Coauthor Index
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