Search Results for author:
Found 5 papers, 1 papers with code
Polaris: A Safety-focused LLM Constellation Architecture for Healthcare
We train our models on proprietary data, clinical care plans, healthcare regulatory documents, medical manuals, and other medical reasoning documents.
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #4 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
Reducing audio membership inference attack accuracy to chance: 4 defenses
It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope.
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks
Adversarial training was introduced as a way to improve the robustness of deep learning models to adversarial attacks.
Deep Speech Denoising with Vector Space Projections
We propose an algorithm to denoise speakers from a single microphone in the presence of non-stationary and dynamic noise.
