48 citations · 109 across the 8 of their papers we have counts for
8 papers
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program
Jeff Druce, James Niehaus, Vanessa Moody +2
The advances in artificial intelligence enabled by deep learning architectures are undeniable. In several cases, deep neural network driven models have surpassed human level perfor…
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems
Jeff Druce, Michael Harradon, James Tittle
We consider the problem of providing users of deep Reinforcement Learning (RL) based systems with a better understanding of when their output can be trusted. We offer an explainabl…
Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation
Shashank Manjunath, Aitzaz Nathaniel, Jeff Druce +1
Recent advances in machine learning (ML) and computer vision tools have enabled applications in a wide variety of arenas such as financial analytics, medical diagnostics, and even…
Privacy preserving Neural Network Inference on Encrypted Data with GPUs
Daniel Takabi, Robert Podschwadt, Jeff Druce +2
Machine Learning as a Service (MLaaS) has become a growing trend in recent years and several such services are currently offered. MLaaS is essentially a set of services that provid…
Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations
Michael Harradon, Jeff Druce, Brian Ruttenberg
Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions…
Improved Support Recovery Guarantees for the Group Lasso With Applications to Structural Health Monitoring
Mojtaba Kadkhodaie Elyaderani, Swayambhoo Jain, Jeffrey Druce +2
This paper considers the problem of estimating an unknown high dimensional signal from noisy linear measurements, {when} the signal is assumed to possess a \emph{group-sparse} stru…