17 citations · 33 across the 4 of their papers we have counts for
5 papers
Continual learning for surface defect segmentation by subnetwork creation and selection
Aleksandr Dekhovich, Miguel A. Bessa
We introduce a new continual (or lifelong) learning algorithm called LDA-CP&S that performs segmentation tasks without undergoing catastrophic forgetting. The method is applied to…
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…
Cooperative data-driven modeling
Aleksandr Dekhovich, O. Taylan Turan, Jiaxiang Yi +1
Data-driven modeling in mechanics is evolving rapidly based on recent machine learning advances, especially on artificial neural networks. As the field matures, new data and models…
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…