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cs.LG2026
Meta-learning to Address Data Shift in Time Series Classification
Samuel Myren, Nidhi Parikh, Natalie Klein
Across engineering and scientific domains, traditional deep learning (TDL) models perform well when training and test data share the same distribution. However, the dynamic nature…
cs.LG2025
The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters
Scott Koermer, Natalie Klein
In scientific applications, predictive modeling is often of limited use without accurate uncertainty quantification (UQ) to indicate when a model may be extrapolating or when more…
cs.LG2025
Regularization via f-Divergence: An Application to Multi-Oxide Spectroscopic Analysis
Weizhi Li, Natalie Klein, Brendan Gifford +3
In this paper, we address the task of characterizing the chemical composition of planetary surfaces using convolutional neural networks (CNNs). Specifically, we seek to predict the…