8 citations · 24 across the 28 of their papers we have counts for
3 papers · 1 filter
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data
Peter Nickl, Lu Xu, Dharmesh Tailor +2
Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training. To simplify such issues, we present the Memory…
Model Merging by Uncertainty-Based Gradient Matching
Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti +2
Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of weight…
The Lie-Group Bayesian Learning Rule
Eren Mehmet Kıral, Thomas Möllenhoff, Mohammad Emtiyaz Khan
The Bayesian Learning Rule provides a framework for generic algorithm design but can be difficult to use for three reasons. First, it requires a specific parameterization of expone…