2 code implementations • 22 Apr 2024 • Steffen Schotthöfer, M. Paul Laiu, Martin Frank, Cory D. Hauck
In this work, we derive and investigate a neural network-based approximation to the entropy closure method to accurately compute the solution of the multi-dimensional moment system with a low memory footprint and competitive computational time.
no code implementations • 30 May 2023 • Emanuele Zangrando, Steffen Schotthöfer, Gianluca Ceruti, Jonas Kusch, Francesco Tudisco
The computing cost and memory demand of deep learning pipelines have grown fast in recent years and thus a variety of pruning techniques have been developed to reduce model parameters.
4 code implementations • 26 May 2022 • Steffen Schotthöfer, Emanuele Zangrando, Jonas Kusch, Gianluca Ceruti, Francesco Tudisco
The main idea is to restrict the weight matrices to a low-rank manifold and to update the low-rank factors rather than the full matrix during training.
