13 citations · 43 across the 10 of their papers we have counts for
11 papers
NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling
Bram Grooten, Farid Hasanov, Chenxiang Zhang +9
Model ensembles have long been a cornerstone for improving generalization and robustness in deep learning. However, their effectiveness often comes at the cost of substantial compu…
Unveiling the Power of Sparse Neural Networks for Feature Selection
Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy +3
Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated p…
Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers
Zahra Atashgahi, Mykola Pechenizkiy, Raymond Veldhuis +1
Efficient time series forecasting has become critical for real-world applications, particularly with deep neural networks (DNNs). Efficiency in DNNs can be achieved through sparse…
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks
Zahra Atashgahi, Xuhao Zhang, Neil Kichler +5
Feature selection that selects an informative subset of variables from data not only enhances the model interpretability and performance but also alleviates the resource demands. R…
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…
Memory-free Online Change-point Detection: A Novel Neural Network Approach
Zahra Atashgahi, Decebal Constantin Mocanu, Raymond Veldhuis +1
Change-point detection (CPD), which detects abrupt changes in the data distribution, is recognized as one of the most significant tasks in time series analysis. Despite the extensi…