43 citations · 87 across the 6 of their papers we have counts for
6 papers
Polaris: A Safety-focused LLM Constellation Architecture for Healthcare
Subhabrata Mukherjee, Paul Gamble, Markel Sanz Ausin +23
We develop Polaris, the first safety-focused LLM constellation for real-time patient-AI healthcare conversations. Unlike prior LLM works in healthcare focusing on tasks like questi…
Reducing audio membership inference attack accuracy to chance: 4 defenses
Michael Lomnitz, Nina Lopatina, Paul Gamble +4
It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope. In membership inference attacks, adver…
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks
Felipe A. Mejia, Paul Gamble, Zigfried Hampel-Arias +4
Adversarial training was introduced as a way to improve the robustness of deep learning models to adversarial attacks. This training method improves robustness against adversarial…
Voices Obscured in Complex Environmental Settings (VOICES) corpus
Colleen Richey, Maria A. Barrios, Zeb Armstrong +11
This paper introduces the Voices Obscured In Complex Environmental Settings (VOICES) corpus, a freely available dataset under Creative Commons BY 4.0. This dataset will promote spe…
Deep Speech Denoising with Vector Space Projections
Jeff Hetherly, Paul Gamble, Maria Barrios +2
We propose an algorithm to denoise speakers from a single microphone in the presence of non-stationary and dynamic noise. Our approach is inspired by the recent success of neural n…
SIG-DB: leveraging homomorphic encryption to Securely Interrogate privately held Genomic DataBases
Alexander J. Titus, Audrey Flower, Patrick Hagerty +6
Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to bio…