1 citations · 1 across the 6 of their papers we have counts for
6 papers
Model Fusion via Retrofitting
Phoomraphee Luenam, Andreas Spanopoulos, Amit Sant +3
Model fusion seeks to combine independently trained neural networks into a single model without retraining, but is complicated by representational divergence arising from permutati…
Local vs Global continual learning
Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe +1
Continual learning is the problem of integrating new information in a model while retaining the knowledge acquired in the past. Despite the tangible improvements achieved in recent…
Landscaping Linear Mode Connectivity
Sidak Pal Singh, Linara Adilova, Michael Kamp +3
The presence of linear paths in parameter space between two different network solutions in certain cases, i.e., linear mode connectivity (LMC), has garnered interest from both theo…
Towards Meta-Pruning via Optimal Transport
Alexander Theus, Olin Geimer, Friedrich Wicke +3
Structural pruning of neural networks conventionally relies on identifying and discarding less important neurons, a practice often resulting in significant accuracy loss that neces…
Towards guarantees for parameter isolation in continual learning
Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe +1
Deep learning has proved to be a successful paradigm for solving many challenges in machine learning. However, deep neural networks fail when trained sequentially on multiple tasks…
Transformer Fusion with Optimal Transport
Moritz Imfeld, Jacopo Graldi, Marco Giordano +3
Fusion is a technique for merging multiple independently-trained neural networks in order to combine their capabilities. Past attempts have been restricted to the case of fully-con…