12 citations · 12 across the 1 of their papers we have counts for
4 papers
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Shiwei Liu, Tianlong Chen, Zahra Atashgahi +6
The success of deep ensembles on improving predictive performance, uncertainty estimation, and out-of-distribution robustness has been extensively studied in the machine learning l…
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…