Search Results for author:
Found 9 papers, 1 papers with code
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.
Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing
In this work, we propose a Bayesian sparse regression for multivariate mixed responses to enhance the prediction of runtime performance metrics and to enable the statistical inferences.
Clustering-based Imputation for Dropout Buyers in Large-scale Online Experimentation
In online experimentation, appropriate metrics (e. g., purchase) provide strong evidence to support hypotheses and enhance the decision-making process.
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.
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning.
JST-RR Model: Joint Modeling of Ratings and Reviews in Sentiment-Topic Prediction
In this work, we propose a probabilistic model to accommodate both textual reviews and overall ratings with consideration of their intrinsic connection for a joint sentiment-topic prediction.
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.
An Improved Modified Cholesky Decomposition Method for Precision Matrix Estimation
In this work, we propose to address the variable order issue in the modified Cholesky decomposition for sparse precision matrix estimation.
Sparse Estimation of Multivariate Poisson Log-Normal Models from Count Data
Modeling data with multivariate count responses is a challenging problem due to the discrete nature of the responses.
