312 citations · 676 across the 20 of their papers we have counts for
5 papers · 1 filter
Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition
Cem Üyük, Mike Lasby, Mohamed Yassin +2
Large neural networks achieve state-of-the-art performance on many tasks, yet their sheer size hinders deployment on resource-constrained devices. Among existing compression approa…
Navigating Extremes: Dynamic Sparsity in Large Output Spaces
Nasib Ullah, Erik Schultheis, Mike Lasby +2
In recent years, Dynamic Sparse Training (DST) has emerged as an alternative to post-training pruning for generating efficient models. In principle, DST allows for a more memory ef…
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias
Aida Mohammadshahi, Yani Ioannou
Knowledge Distillation is a commonly used Deep Neural Network (DNN) compression method, which often maintains overall generalization performance. However, we show that even for bal…
Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems
Farzaneh Dehghani, Mahsa Dibaji, Fahim Anzum +23
Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, fro…
Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks
Mahdi Mohammadizadeh, Arash Mozhdehi, Yani Ioannou +1
Although many real-world applications, such as disease prediction, and fault detection suffer from class imbalance, most existing graph-based classification methods ignore the skew…