Nathan Drenkow

According to our database, Nathan Drenkow authored at least 20 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments.

Kanyifeechukwu Jane Oguine, Roger D. Soberanis-Mukul, Nathan Drenkow, Mathias Unberath

CoRR, 2024

RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning.

Nathan Drenkow, Mathias Unberath

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023

Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence.

Erik C. Johnson, Brian S. Robinson, Gautam K. Vallabha, Justin Joyce, Jordan K. Matelsky, Raphael Norman-Tenazas, Isaac Western, Marisel Villafañe-Delgado, Martha Cervantes, Michael S. Robinette, Arun V. Reddy, Lindsey Kitchell, Patricia K. Rivlin, Elizabeth Perez Reilly, Nathan Drenkow, Matthew J. Roos, I-Jeng Wang, Brock A. Wester, William R. Gray Roncal, Joan A. Hoffmann

CoRR, 2023

Do Adaptive Active Attacks Pose Greater Risk Than Static Attacks?

Nathan Drenkow, Max Lennon, I-Jeng Wang, Philippe Burlina

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

A sparse null code emerges in deep neural networks.

Brian S. Robinson, Nathan Drenkow, Colin Conwell, Michael F. Bonner

Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models, 2023

Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models.

Mitchell Pavlak, Nathan Drenkow, Nicholas Petrick, Mohammad Mehdi Farhangi, Mathias Unberath

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

2022

Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity.

Nathan Drenkow, Alvin Tan, Chace Ashcraft, Kiran Karra

CoRR, 2022

Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis.

Nathan Drenkow, Neil Fendley, Philippe Burlina

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

2021

Robustness in Deep Learning for Computer Vision: Mind the gap?

Nathan Drenkow, Numair Sani, Ilya Shpitser, Mathias Unberath

CoRR, 2021

On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation.

Zhaoshuo Li, Nathan Drenkow, Hao Ding, Andy S. Ding, Alexander Lu, Francis X. Creighton, Russell H. Taylor, Mathias Unberath

CoRR, 2021

Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?

Max Lennon, Nathan Drenkow, Philippe Burlina

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers.

Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy S. Ding, Francis X. Creighton, Russell H. Taylor, Mathias Unberath

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Addressing Visual Search in Open and Closed Set Settings.

Nathan Drenkow, Philippe Burlina, Neil Fendley, Onyekachi Odoemene, Jared Markowitz

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

Objectness-Guided Open Set Visual Search and Closed Set Detection.

Nathan Drenkow, Philippe Burlina, Neil Fendley, Kachi Odoemene, Jared Markowitz

CoRR, 2020

Random Projections for Adversarial Attack Detection.

Nathan Drenkow, Neil Fendley, Philippe Burlina

CoRR, 2020

Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity.

Nathan Drenkow, Justin Joyce, Jordan Matelsky, Jennifer Heiko, Reem Larabi, Brock A. Wester, Dean Kleissas, William R. Gray Roncal

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Jacks of All Trades, Masters of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks.

Neil Fendley, Max Lennon, I-Jeng Wang, Philippe Burlina, Nathan Drenkow

Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

2019

The First International Competition in Machine Reconnaissance Blind Chess.

Ryan W. Gardner, Corey Lowman, Casey Richardson, Ashley J. Llorens, Jared Markowitz, Nathan Drenkow, Andrew Newman, Gregory Clark, Gino Perrotta, Robert Perrotta, Timothy Highley, Vlad Shcherbina, William Bernadoni, Mark Jordan, Asen Asenov

Proceedings of the NeurIPS 2019 Competition and Demonstration Track, 2019

2014

Selection of universal features for image classification.

Pedro A. Rodriguez, Nathan Drenkow, Daniel DeMenthon, Zachary H. Koterba, Kathleen Kauffman, Duane Cornish, Bart L. Paulhamus, R. Jacob Vogelstein

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2014

2013

A practical test set bound for rank learning.

Nathan Drenkow, Philippe Burlina, I-Jeng Wang, Daniel DeMenthon, Craig Carmen

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2013