83 citations · 230 across the 9 of their papers we have counts for
4 papers · 1 filter
Interpolating neural network: A novel unification of machine learning and interpolation theory
Chanwook Park, Sourav Saha, Jiachen Guo +8
Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, a…
iPINNs: Incremental learning for Physics-informed neural networks
Aleksandr Dekhovich, Marcel H. F. Sluiter, David M. J. Tax +1
Physics-informed neural networks (PINNs) have recently become a powerful tool for solving partial differential equations (PDEs). However, finding a set of neural network parameters…
Continual Prune-and-Select: Class-incremental learning with specialized subnetworks
Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter +1
The human brain is capable of learning tasks sequentially mostly without forgetting. However, deep neural networks (DNNs) suffer from catastrophic forgetting when learning one task…
Neural network relief: a pruning algorithm based on neural activity
Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter +1
Current deep neural networks (DNNs) are overparameterized and use most of their neuronal connections during inference for each task. The human brain, however, developed specialized…