5 citations · 6 across the 15 of their papers we have counts for
3 papers · 1 filter
Joint Model and Data Sparsification via the Marginal Likelihood
Alexander Timans, Thomas Möllenhoff, Christian A. Naesseth +2
Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in the principle of automatic rel…
Variational Learning Finds Flatter Solutions at the Edge of Stability
Avrajit Ghosh, Bai Cong, Rio Yokota +5
Variational Learning (VL) has recently gained popularity for training deep neural networks. Part of its empirical success can be explained by theories such as PAC-Bayes bounds, min…
Natural Variational Annealing for Multimodal Optimization
Tâm LeMinh, Julyan Arbel, Thomas Möllenhoff +2
We introduce a new multimodal optimization approach called Natural Variational Annealing (NVA) that combines the strengths of three foundational concepts to simultaneously search f…