32 citations · 43 across the 10 of their papers we have counts for
5 papers · 1 filter
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs
Gokul Bhusal, Kevin Miller, Ekaterina Merkurjev
Active learning enhances the performance of machine learning methods, particularly in semi-supervised cases, by judiciously selecting a limited number of unlabeled data points for…
Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data
Nicole Hayes, Ekaterina Merkurjev, Guo-Wei Wei
Data sets with imbalanced class sizes, where one class size is much smaller than that of others, occur exceedingly often in many applications, including those with biological found…
Persistent Laplacian-enhanced Algorithm for Scarcely Labeled Data Classification
Gokul Bhusal, Ekaterina Merkurjev, Guo-Wei Wei
The success of many machine learning (ML) methods depends crucially on having large amounts of labeled data. However, obtaining enough labeled data can be expensive, time-consuming…
Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular Data
Nicole Hayes, Ekaterina Merkurjev, Guo-Wei Wei
In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints. Consequently, one often faces the challenging task of pre…
Multiscale Laplacian Learning
Ekaterina Merkurjev, Duc DUy Nguyen, Guo-Wei Wei
Machine learning methods have greatly changed science, engineering, finance, business, and other fields. Despite the tremendous accomplishments of machine learning and deep learnin…