1 citations · 1 across the 10 of their papers we have counts for
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Trustworthiness of Stochastic Gradient Descent in Distributed Learning
Hongyang Li, Caesar Wu, Mohammed Chadli +2
Distributed learning (DL) uses multiple nodes to accelerate training, enabling efficient optimization of large-scale models. Stochastic Gradient Descent (SGD), a key optimization a…
Dataset | Mindset = Explainable AI | Interpretable AI
Caesar Wu, Rajkumar Buyya, Yuan Fang Li +1
We often use "explainable" Artificial Intelligence (XAI)" and "interpretable AI (IAI)" interchangeably when we apply various XAI tools for a given dataset to explain the reasons th…
Heterogeneity: An Open Challenge for Federated On-board Machine Learning
Maria Hartmann, Grégoire Danoy, Pascal Bouvry
The design of satellite missions is currently undergoing a paradigm shift from the historical approach of individualised monolithic satellites towards distributed mission configura…
Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting
Jingjing Xu, Caesar Wu, Yuan-Fang Li +2
Alongside the continuous process of improving AI performance through the development of more sophisticated models, researchers have also focused their attention to the emerging con…
Training Green AI Models Using Elite Samples
Mohammed Alswaitti, Roberto Verdecchia, Grégoire Danoy +2
The substantial increase in AI model training has considerable environmental implications, mandating more energy-efficient and sustainable AI practices. On the one hand, data-centr…
Transformer Multivariate Forecasting: Less is More?
Jingjing Xu, Caesar Wu, Yuan-Fang Li +1
In the domain of multivariate forecasting, transformer models stand out as powerful apparatus, displaying exceptional capabilities in handling messy datasets from real-world contex…