4 citations · 10 across the 3 of their papers we have counts for
6 papers
Where to Pay Attention in Sparse Training for Feature Selection?
Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy +1
A new line of research for feature selection based on neural networks has recently emerged. Despite its superiority to classical methods, it requires many training iterations to co…
Self-Attention Meta-Learner for Continual Learning
Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy
Continual learning aims to provide intelligent agents capable of learning multiple tasks sequentially with neural networks. One of its main challenging, catastrophic forgetting, is…
Learning Invariant Representation for Continual Learning
Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy
Continual learning aims to provide intelligent agents that are capable of learning continually a sequence of tasks, building on previously learned knowledge. A key challenge in thi…
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
Zahra Atashgahi, Ghada Sokar, Tim van der Lee +4
Major complications arise from the recent increase in the amount of high-dimensional data, including high computational costs and memory requirements. Feature selection, which iden…
Topological Insights into Sparse Neural Networks
Shiwei Liu, Tim Van der Lee, Anil Yaman +5
Sparse neural networks are effective approaches to reduce the resource requirements for the deployment of deep neural networks. Recently, the concept of adaptive sparse connectivit…
SpaceNet: Make Free Space For Continual Learning
Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy
The continual learning (CL) paradigm aims to enable neural networks to learn tasks continually in a sequential fashion. The fundamental challenge in this learning paradigm is catas…