Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Publication
- Publication Type
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Robust And Fair Machine Learning Under Distribution Shift, Wei Du
Robust And Fair Machine Learning Under Distribution Shift, Wei Du
Graduate Theses and Dissertations
Machine learning algorithms have been widely used in real world applications. The development of these techniques has brought huge benefits for many AI-related tasks, such as natural language processing, image classification, video analysis, and so forth. In traditional machine learning algorithms, we usually assume that the training data and test data are independently and identically distributed (iid), indicating that the model learned from the training data can be well applied to the test data with good prediction performance. However, this assumption is quite restrictive because the distribution shift can exist from the training data to the test data in many …
Artificial Intelligence System For Automatic Imaging, Quantification, And Identification Of Arthropods In Leaf Litter And Pitfall Samples, Pierce Helton, Khoa Luu, Ashley Dowling
Artificial Intelligence System For Automatic Imaging, Quantification, And Identification Of Arthropods In Leaf Litter And Pitfall Samples, Pierce Helton, Khoa Luu, Ashley Dowling
Inquiry: The University of Arkansas Undergraduate Research Journal
It is well known that arthropods are the most diverse and abundant eukaryotic organisms on the planet. Museum and research collections have huge insect accumulations from expeditions conducted over history that contain specimens of both temporal and spatial value, including hundreds of thousands of species. This biodiversity data is inaccessible to the research community, resulting in a vast amount of “dark data”. The primary objective of this study is to develop an artificial intelligence-driven system for specimen identification that greatly minimizes the time and expertise required to identify specimens in atypical environments. Successful development will have profound impacts on both …