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The Matrix: A Bayesian learning model for LLMs
In this paper, we introduce a Bayesian learning model to understand the behavior of Large Language Models (LLMs).
Predicting Ground Reaction Force from Inertial Sensors
Our aim in this paper is to determine if data collected with inertial measurement units (IMUs), that can be worn by athletes during outdoor runs, can be used to predict GRF with sufficient accuracy to allow the analysis of its derived biomechanical variables (e. g., contact time and loading rate).
Differentially Private Synthetic Control
In this work, we provide the first algorithms for differentially private synthetic control with explicit error bounds.
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