Nathan Drenkow

32 papers A* 1A 4Journal 19Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Trojans in Artificial Intelligence (TrojAI) Final Report. CoRR Kristopher W. Reese, Taylor Kulp-McDowall, Michael Majurski, Timothy Blattner, Derek Juba, Peter Bajcsy, Antonio Cardone, Philippe Dessauw, Alden Dima, Anthony J. Kearsley, Melinda Kleczynski, Joel Vasanth, Walid Keyrouz, Chace Ashcraft, Neil Fendley, Ted Staley, Trevor Stout, Josh Carney, Greg Canal, William Redman, Aurora Schmidt, Cameron Hickert, William Paul, Jared Markowitz, Nathan Drenkow, David Shriver, Marissa Connor, Keltin Grimes, Marco Christiani, Hayden Moore, Jordan Widjaja, Kasimir Gabert, Uma Balakrishnan, Satyanadh Gundimada, John Jacobellis, Sandya Lakkur, Vitus Leung, Jon Roose, Casey Battaglino, Farinaz Koushanfar, Greg Fields, Xihe Gu, Yaman Jandali, Xinqiao Zhang, Tara Javidi, Akash Vartak, Tim Oates, Ben Erichson, Michael Mahoney, Rauf Izmailov, Xiangyu Zhang, Guangyu Shen, Siyuan Cheng, Shiqing Ma, Xiaofeng Wang, Haixu Tang, Di Tang, Xiaoyi Chen, Zihao Wang, Rui Zhu, Susmit Jha, Xiao Lin, Manoj Acharya, Weichao Zhou, Feisi Fu, Panagiota Kiourti, Chenyu Wang, Zijian Guo, H. M. Sabbir Ahmad, Wenchao Li, Chao Chen
2025	J	`jnl`	A Causal Framework for Aligning Image Quality Metrics and Deep Neural Network Robustness. CoRR Nathan Drenkow, Mathias Unberath
2025	J	`jnl`	A Systematic Review of Poisoning Attacks Against Large Language Models. CoRR Neil Fendley, Edward W. Staley, Joshua Carney, William Redman, Marie Chau, Nathan Drenkow
2025	J	`jnl`	Backdoors in DRL: Four Environments Focusing on In-distribution Triggers. CoRR Chace Ashcraft, Ted Staley, Josh Carney, Cameron Hickert, Kiran Karra, Nathan Drenkow
2025	J	`jnl`	Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework. CoRR Nathan Drenkow, Mitchell Pavlak, Keith Harrigian, Ayah Zirikly, Adarsh Subbaswamy, Mathias Unberath
2025	J	`jnl`	Investigating the Treacherous Turn in Deep Reinforcement Learning. CoRR Chace Ashcraft, Kiran Karra, Josh Carney, Nathan Drenkow
2025	J	`jnl`	Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling. CoRR Benjamin D. Killeen, Bohua Wan, Aditya V. Kulkarni, Nathan Drenkow, Michael Oberst, Paul H. Yi, Mathias Unberath
2024	J	`jnl`	Causality-Driven Audits of Model Robustness. CoRR Nathan Drenkow, Chris Ribaudo, Mathias Unberath
2024	J	`jnl`	From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments. CoRR Kanyifeechukwu Jane Oguine, Roger D. Soberanis-Mukul, Nathan Drenkow, Mathias Unberath
2024	A	`conf`	RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning. WACV Nathan Drenkow, Mathias Unberath
2023	—	`conf`	A sparse null code emerges in deep neural networks. UniReps Brian S. Robinson, Nathan Drenkow, Colin Conwell, Michael F. Bonner
2023	—	`conf`	Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models. MICCAI (3) Mitchell Pavlak, Nathan Drenkow, Nicholas Petrick, Mohammad Mehdi Farhangi, Mathias Unberath
2023	J	`jnl`	Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models. CoRR Mitchell Pavlak, Nathan Drenkow, Nicholas Petrick, Mohammad Mehdi Farhangi, Mathias Unberath
2023	A	`conf`	Do Adaptive Active Attacks Pose Greater Risk Than Static Attacks? WACV Nathan Drenkow, Max Lennon, I-Jeng Wang, Philippe Burlina
2023	J	`jnl`	Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence. CoRR 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
2023	J	`jnl`	RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning. CoRR Nathan Drenkow, Mathias Unberath
2022	A	`conf`	Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis. WACV Nathan Drenkow, Neil Fendley, Philippe Burlina
2022	J	`jnl`	Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity. CoRR Nathan Drenkow, Alvin Tan, Chace Ashcraft, Kiran Karra
2021	—	`conf`	Addressing Visual Search in Open and Closed Set Settings. CVPR Workshops Nathan Drenkow, Philippe Burlina, Neil Fendley, Onyekachi Odoemene, Jared Markowitz
2021	J	`jnl`	On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation. CoRR Zhaoshuo Li, Nathan Drenkow, Hao Ding, Andy S. Ding, Alexander Lu, Francis X. Creighton, Russell H. Taylor, Mathias Unberath
2021	—	`conf`	Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose? ICCVW Max Lennon, Nathan Drenkow, Philippe Burlina
2021	J	`jnl`	Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose? CoRR Max Lennon, Nathan Drenkow, Philippe Burlina
2021	A*	`conf`	Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers. ICCV Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy S. Ding, Francis X. Creighton, Russell H. Taylor, Mathias Unberath
2021	J	`jnl`	Robustness in Deep Learning for Computer Vision: Mind the gap? CoRR Nathan Drenkow, Numair Sani, Ilya Shpitser, Mathias Unberath
2020	J	`jnl`	Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks. CoRR Neil Fendley, Max Lennon, I-Jeng Wang, Philippe Burlina, Nathan Drenkow
2020	—	`conf`	Jacks of All Trades, Masters of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks. ECCV Workshops (1) Neil Fendley, Max Lennon, I-Jeng Wang, Philippe Burlina, Nathan Drenkow
2020	—	`conf`	Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity. MICCAI (5) Nathan Drenkow, Justin Joyce, Jordan Matelsky, Jennifer Heiko, Reem Larabi, Brock A. Wester, Dean Kleissas, William R. Gray Roncal
2020	J	`jnl`	Objectness-Guided Open Set Visual Search and Closed Set Detection. CoRR Nathan Drenkow, Philippe Burlina, Neil Fendley, Kachi Odoemene, Jared Markowitz
2020	J	`jnl`	Random Projections for Adversarial Attack Detection. CoRR Nathan Drenkow, Neil Fendley, Philippe Burlina
2019	—	`conf`	The First International Competition in Machine Reconnaissance Blind Chess. NeurIPS (Competition and Demos) 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
2014	A	`conf`	Selection of universal features for image classification. WACV Pedro A. Rodriguez, Nathan Drenkow, Daniel DeMenthon, Zachary H. Koterba, Kathleen Kauffman, Duane Cornish, Bart L. Paulhamus, R. Jacob Vogelstein
2013	—	`conf`	A practical test set bound for rank learning. MLSP Nathan Drenkow, Philippe Burlina, I-Jeng Wang, Daniel DeMenthon, Craig Carmen