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cs.LG2025
Test Time Adaptation Using Adaptive Quantile Recalibration
Paria Mehrbod, Pedro Vianna, Geraldin Nanfack +2
Domain adaptation is a key strategy for enhancing the generalizability of deep learning models in real-world scenarios, where test distributions often diverge significantly from th…
cs.LG2025
From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging
Stefan Horoi, Guy Wolf, Eugene Belilovsky +1
Modern deep learning is increasingly characterized by the use of open-weight foundation models that can be fine-tuned on specialized datasets. This has led to a proliferation of ex…