12 citations · 12 across the 5 of their papers we have counts for
5 papers · 1 filter
Are Sparse Neural Networks Better Hard Sample Learners?
Qiao Xiao, Boqian Wu, Lu Yin +4
While deep learning has demonstrated impressive progress, it remains a daunting challenge to learn from hard samples as these samples are usually noisy and intricate. These hard sa…
LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration
Camiel Oerlemans, Bram Grooten, Michiel Braat +3
Predicting the behavior of road users accurately is crucial to enable the safe operation of autonomous vehicles in urban or densely populated areas. Therefore, there has been a gro…
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…
Dynamic Data Pruning for Automatic Speech Recognition
Qiao Xiao, Pingchuan Ma, Adriana Fernandez-Lopez +7
The recent success of Automatic Speech Recognition (ASR) is largely attributed to the ever-growing amount of training data. However, this trend has made model training prohibitivel…
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…