Marissa Connor

15 papers A* 2A 1Misc 1Journal 10

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
2024	J	`jnl`	Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing. CoRR Keltin Grimes, Marco Christiani, David Shriver, Marissa Connor
2024	J	`jnl`	Learning Internal Representations of 3D Transformations From 2D Projected Inputs. Neural Comput. Marissa Connor, Bruno A. Olshausen, Christopher Rozell
2024	J	`jnl`	The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability. CoRR Stephen Casper, Jieun Yun, Joonhyuk Baek, Yeseong Jung, Minhwan Kim, Kiwan Kwon, Saerom Park, Hayden Moore, David Shriver, Marissa Connor, Keltin Grimes, Angus Nicolson, Arush Tagade, Jessica Rumbelow, Hieu Minh Nguyen, Dylan Hadfield-Menell
2023	J	`jnl`	Learning Internal Representations of 3D Transformations from 2D Projected Inputs. CoRR Marissa Connor, Bruno A. Olshausen, Christopher Rozell
2022	J	`jnl`	Rethinking Backdoor Data Poisoning Attacks in the Context of Semi-Supervised Learning. CoRR Marissa Connor, Vincent Emanuele
2022	—	`—`	Structured Learning with Manifold Representations of Natural Data Variations. Marissa Connor
2021	J	`jnl`	Learning Identity-Preserving Transformations on Data Manifolds. CoRR Marissa Connor, Kion Fallah, Christopher Rozell
2021	A	`conf`	Variational Autoencoder with Learned Latent Structure. AISTATS Marissa Connor, Gregory Canal, Christopher Rozell
2020	A*	`conf`	Generative causal explanations of black-box classifiers. NeurIPS Matthew R. O'Shaughnessy, Gregory Canal, Marissa Connor, Christopher Rozell, Mark A. Davenport
2020	J	`jnl`	Generative causal explanations of black-box classifiers. CoRR Matthew R. O'Shaughnessy, Gregory Canal, Marissa Connor, Mark A. Davenport, Christopher Rozell
2020	A*	`conf`	Representing Closed Transformation Paths in Encoded Network Latent Space. AAAI Marissa Connor, Christopher Rozell
2020	Misc	`conf`	The Picasso Algorithm for Bayesian Localization Via Paired Comparisons in a Union of Subspaces Model. ICASSP Gregory Canal, Marissa Connor, Jihui Jin, Namrata Nadagouda, Matthew R. O'Shaughnessy, Christopher J. Rozell, Mark A. Davenport
2020	J	`jnl`	Variational Autoencoder with Learned Latent Structure. CoRR Marissa Connor, Gregory H. Canal, Christopher J. Rozell
2019	J	`jnl`	Representing Closed Transformation Paths in Encoded Network Latent Space. CoRR Marissa Connor, Christopher Rozell