3 papers
cs.LG2026
Building The Ph(ysical)AI Layer Of Machine Intelligence
Ulbert Jose Botero, Liam Smith, Brooks Olney +5
Foundation models achieve generalization through massive-scale training on diverse data, but have limitations with transfer to truly unseen domains without paired training data. We…
cs.CV2026
From Snow to Rain: Evaluating Robustness, Calibration, and Complexity of Model-Based Robust Training
Josué MartÃnez-MartÃnez, Olivia Brown, Giselle Zeno +2
Robustness to natural corruptions remains a critical challenge for reliable deep learning, particularly in safety-sensitive domains. We study a family of model-based training appro…
cs.CV2025
LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
Danielle Sullivan-Pao, Nicole Tian, Pooya Khorrami
As generative AI image technologies become more widespread and advanced, there is a growing need for strong attribution models. These models are crucial for verifying the authentic…