3 papers
physics.chem-ph2026
Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
Vivienne Pelletier, Vedant Bhat, Daniel J. Rivera +2
Machine-learned interatomic potentials (MLIPs), particularly graph neural network (GNN)-based models, offer a promising route to achieving near-density functional theory (DFT) accu…
cs.LG2026
Active Transfer Bagging: A New Approach for Accelerated Active Learning Acquisition of Data by Combined Transfer Learning and Bagging Based Models
Vivienne Pelletier, Daniel J. Rivera, Obinna Nwokonkwo +2
Modern machine learning has achieved remarkable success on many problems, but this success often depends on the existence of large, labeled datasets. While active learning can dram…
cs.IR2018
Viscovery: Trend Tracking in Opinion Forums based on Dynamic Topic Models
Ignacio Espinoza, Marcelo Mendoza, Pablo Ortega +2
Opinions in forums and social networks are released by millions of people due to the increasing number of users that use Web 2.0 platforms to opine about brands and organizations.…