544 citations · 552 across the 8 of their papers we have counts for
9 papers
Beyond Masked Sparsity: SNACK Enables Truly Sparse Neural Networks on GPU
Jafar Badour, Maurice van Keulen, Elena Mocanu
Deep neural networks continue to grow in parameter count, driving up training and inference cost on GPUs. Sparse neural networks and Dynamic Sparse Training (DST) promise to reduce…
When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
Boqian Wu, Qiao Xiao, Patrik Okanovic +6
Scaling laws for dense LLMs under infinite data are well explored, but how sparsity interacts with limited data is not. In this work, we study sparse training in data-constrained r…
Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling
Qiao Xiao, Boqian Wu, Patrik Okanovic +6
Dynamic Sparse Training (DST) offers a promising paradigm for improving the training and inference efficiency of deep neural networks; however, we find that in large language model…
C-SHAP for time series: An approach to high-level temporal explanations
Annemarie Jutte, Faizan Ahmed, Jeroen Linssen +1
In high-stakes domains, such as healthcare and industry, the explainability of AI-based decision-making has become crucial. Without insight into model reasoning, the reliability of…
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness
Boqian Wu, Qiao Xiao, Shunxin Wang +5
It is generally perceived that Dynamic Sparse Training opens the door to a new era of scalability and efficiency for artificial neural networks at, perhaps, some costs in accuracy…
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
Boqian Wu, Qiao Xiao, Shiwei Liu +5
Deep neural networks have evolved as the leading approach in 3D medical image segmentation due to their outstanding performance. However, the ever-increasing model size and computa…
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Meike Nauta, Jan Trienes, Shreyasi Pathak +6
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning…
A probabilistic database approach to autoencoder-based data cleaning
R. R. Mauritz, F. P. J. Nijweide, J. Goseling +1
Data quality problems are a large threat in data science. In this paper, we propose a data-cleaning autoencoder capable of near-automatic data quality improvement. It learns the st…
Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques
Jesper Provoost, Luc Wismans, Sander Van der Drift +2
Public road authorities and private mobility service providers need information derived from the current and predicted traffic states to act upon the daily urban system and its spa…