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Nathan Drenkow
Nathan Drenkow
Research Scientist - Computer Vision, The Johns Hopkins University, Applied Physics Laboratory
Verified email at jhu.edu
Title
Cited by
Cited by
Year
Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers
Z Li, X Liu, N Drenkow, A Ding, FX Creighton, RH Taylor, M Unberath
Proceedings of the IEEE/CVF international conference on computer vision …, 2021
2202021
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
N Drenkow, N Sani, I Shpitser, M Unberath
arXiv preprint arXiv:2112.00639, 2021
702021
A framework for rigorous evaluation of human performance in human and machine learning comparison studies
HP Cowley, M Natter, K Gray-Roncal, RE Rhodes, EC Johnson, ...
Scientific Reports 12 (1), 5444, 2022
112022
Bioimage informatics for big data
H Peng, J Zhou, Z Zhou, A Bria, Y Li, DM Kleissas, NG Drenkow, B Long, ...
Focus on Bio-Image Informatics, 263-272, 2016
102016
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis
N Drenkow, N Fendley, P Burlina
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022
9*2022
The first international competition in machine reconnaissance blind chess
RW Gardner, C Lowman, C Richardson, AJ Llorens, J Markowitz, ...
NeurIPS 2019 Competition and Demonstration Track, 121-130, 2020
92020
Patch attack invariance: How sensitive are patch attacks to 3d pose?
M Lennon, N Drenkow, P Burlina
Proceedings of the IEEE/CVF International Conference on Computer Vision, 112-121, 2021
82021
Jacks of all trades, masters of none: addressing distributional shift and obtrusiveness via transparent patch attacks
N Fendley, M Lennon, IJ Wang, P Burlina, N Drenkow
Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020
82020
Toward a reproducible, scalable framework for processing large neuroimaging datasets
EC Johnson, M Wilt, LM Rodriguez, R Norman-Tenazas, C Rivera, ...
BioRxiv, 615161, 2019
82019
On the sins of image synthesis loss for self-supervised depth estimation
Z Li, N Drenkow, H Ding, AS Ding, A Lu, FX Creighton, RH Taylor, ...
arXiv preprint arXiv:2109.06163, 2021
62021
Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets
EC Johnson, M Wilt, LM Rodriguez, R Norman-Tenazas, C Rivera, ...
GigaScience 9 (12), giaa147, 2020
62020
Selection of universal features for image classification
PA Rodriguez, N Drenkow, D DeMenthon, Z Koterba, K Kauffman, ...
IEEE Winter Conference on Applications of Computer Vision, 355-362, 2014
42014
Comparing user experiences in 2D and 3D videoconferencing
SS Hemami, FM Ciaramello, SS Chen, NG Drenkow, DY Lee, S Lee, ...
2012 19th IEEE International Conference on Image Processing, 1969-1972, 2012
42012
Circuit summer program: A computational neuroscience outreach experience for high-achieving undergraduates via sponsored research
M Encarnacion, C Bishop, J Downs, N Drenkow, JK Matelsky, PK Rivlin, ...
2018 IEEE Integrated STEM Education Conference (ISEC), 45-52, 2018
32018
Benchmarking Human Performance for Visual Search of Aerial Images
RE Rhodes, HP Cowley, JG Huang, W Gray-Roncal, BA Wester, ...
Frontiers in Psychology 12, 733021, 2021
22021
A sparse null code emerges in deep neural networks
BS Robinson, N Drenkow, C Conwell, M Bonner
UniReps: the First Workshop on Unifying Representations in Neural Models, 2023
12023
Semi-supervised domain transfer for robust maritime satellite image classification
PR Emmanuel, CR Ratto, NG Drenkow, JJ Markowitz
Artificial Intelligence and Machine Learning for Multi-Domain Operations …, 2023
12023
Exploiting large neuroimaging datasets to create connectome-constrained approaches for more robust, efficient, and adaptable artificial intelligence
EC Johnson, BS Robinson, GK Vallabha, J Joyce, JK Matelsky, ...
Artificial Intelligence and Machine Learning for Multi-Domain Operations …, 2023
12023
Cortical Transformers: Robustness and Model Compression with Multi-Scale Connectivity Properties of the Neocortex.
BS Robinson, N Drenkow
SVRHM 2022 Workshop@ NeurIPS, 2022
12022
Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity
N Drenkow, J Joyce, J Matelsky, J Heiko, R Larabi, B Wester, D Kleissas, ...
Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020
12020
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