3 papers
cs.LG2026
Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate
Mathew Mithra Noel, Arindam Banerjee, Yug D. Oswal +2
Most real-world datasets used for training supervised learning models are contaminated with noisy data and outliers leading to large prediction errors. This paper proposes a new ap…
cs.LG2025
Geospatial Machine Learning Libraries
Adam J. Stewart, Caleb Robinson, Arindam Banerjee
Recent advances in machine learning have been supported by the emergence of domain-specific software libraries, enabling streamlined workflows and increased reproducibility. For ge…
cs.CV2024
On the Generalizability of Foundation Models for Crop Type Mapping
Yi-Chia Chang, Adam J. Stewart, Favyen Bastani +5
Foundation models pre-trained using self-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, including language understanding, text…