4 citations · 8 across the 7 of their papers we have counts for
8 papers
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 +17
Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense a…
A Risk-Sensitive Approach to Policy Optimization
Jared Markowitz, Ryan W. Gardner, Ashley Llorens +2
Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-mak…
Out-of-Distribution Robustness with Deep Recursive Filters
Kapil D. Katyal, I-Jeng Wang, Gregory D. Hager
Accurate state and uncertainty estimation is imperative for mobile robots and self driving vehicles to achieve safe navigation in pedestrian rich environments. A critical component…
Learning a Group-Aware Policy for Robot Navigation
Kapil Katyal, Yuxiang Gao, Jared Markowitz +4
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mos…
Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks
Neil Fendley, Max Lennon, I-Jeng Wang +2
We focus on the development of effective adversarial patch attacks and -- for the first time -- jointly address the antagonistic objectives of attack success and obtrusiveness via…
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection
William Paul, I-Jeng Wang, Fady Alajaji +1
Our work focuses on unsupervised and generative methods that address the following goals: (a) learning unsupervised generative representations that discover latent factors controll…
Adversarial Examples in Remote Sensing
Wojciech Czaja, Neil Fendley, Michael Pekala +2
This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by c…
Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning
Jared Markowitz, Aurora C. Schmidt, Philippe M. Burlina +1
We address zero-shot (ZS) learning, building upon prior work in hierarchical classification by combining it with approaches based on semantic attribute estimation. For both non-nov…