Abstract Large Language Models (LLMs) have the capacity to store and recall facts. Through
experimentation with open-source models, we observe that this ability to retrieve facts can …

Trauma is a significant cause of mortality and diagnostic methods for traumatic injuries, such
as X-rays, CT scans, and MRI, are often time-consuming and dependent on medical …

Training neural networks is a fundamental problem in theoretical machine learning. Second-
order methods are rarely used in practice due to their high computational cost, even they …

We introduce a refined differentially private (DP) data structure for kernel density estimation
(KDE), offering not only improved privacy-utility tradeoff but also better efficiency over prior …

We introduce a principled approach to Outlier-Efficient Attention Layers via associative
memory models to reduce outlier emergence in large transformer-based model. Our main …

Predicting human mobility across multiple cities presents significant challenges due to the
complex and diverse spatial-temporal dynamics inherent in different urban environments. In …

Foundation models have revolutionized artificial intelligence, yet fundamental challenges
remain in understanding and optimizing their capabilities in adaptation and inference. This …