Search Results for author:
Found 9 papers, 4 papers with code
StatLLM: A Dataset for Evaluating the Performance of Large Language Models in Statistical Analysis
A major challenge in this evaluation lies in the absence of a benchmark dataset for statistical code (e. g., SAS and R).
Performance Evaluation of Large Language Models in Statistical Programming
This study offers valuable insights into the capabilities and limitations of LLMs in statistical programming, providing guidance for future advancements in AI-assisted coding systems for statistical analysis.
Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability
To address this gap, this paper focuses on conducting a comprehensive review of available AI reliability data and establishing DR-AIR: a data repository for AI reliability.
A Comprehensive Case Study on the Performance of Machine Learning Methods on the Classification of Solar Panel Electroluminescence Images
In this paper, we comprehensively compare different machine learning and deep learning methods under different performance metrics on the classification of solar cell EL images from monocrystalline and polycrystalline modules.
Planning Reliability Assurance Tests for Autonomous Vehicles
This paper aims to fill in this gap by developing statistical methods for planning AV reliability assurance tests based on recurrent events data.
Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data
We demonstrate that deep neural networks can successfully automate the classification of an image into a known species group for which it has been trained.
Statistical Perspectives on Reliability of Artificial Intelligence Systems
We also describe recent developments in modeling and analysis of AI reliability and outline statistical research challenges in this area, including out-of-distribution detection, the effect of the training set, adversarial attacks, model accuracy, and uncertainty quantification, and discuss how those topics can be related to AI reliability, with illustrative examples.
Reliability Analysis of Artificial Intelligence Systems Using Recurrent Events Data from Autonomous Vehicles
In this paper, we use recurrent disengagement events as a representation of the reliability of the AI system in AV, and propose a statistical framework for modeling and analyzing the recurrent events data from AV driving tests.
Investigating the Robustness of Artificial Intelligent Algorithms with Mixture Experiments
Artificial intelligent (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including computer vision, autonomous driving, and medical diagnostics.
