no code implementations • 10 Apr 2023 • Aleksandr Dekhovich, Marcel H. F. Sluiter, David M. J. Tax, Miguel A. Bessa
Physics-informed neural networks (PINNs) have recently become a powerful tool for solving partial differential equations (PDEs).
1 code implementation • 9 Aug 2022 • Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter, Miguel A. Bessa
In particular, CP&S is capable of sequentially learning 10 tasks from ImageNet-1000 keeping an accuracy around 94% with negligible forgetting, a first-of-its-kind result in class-incremental learning.
1 code implementation • 22 Sep 2021 • Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter, Miguel A. Bessa
Current deep neural networks (DNNs) are overparameterized and use most of their neuronal connections during inference for each task.