44 citations · 107 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
Novelty Producing Synaptic Plasticity
Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu +2
A learning process with the plasticity property often requires reinforcement signals to guide the process. However, in some tasks (e.g. maze-navigation), it is very difficult (or i…
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring
Sibylle Hess, Wouter Duivesteijn, Decebal Mocanu
We formally prove the connection between k-means clustering and the predictions of neural networks based on the softmax activation layer. In existing work, this connection has been…