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Invited Talk: Hybrid AI/HPC Approaches and Linear Algebra
SessionWorkshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems (ScalAH'22)
DescriptionWe present a brief overview of machine learning techniques and show that certain methods of linear algebra such as the eigenvalue problem or more generally singular value decomposition constitute the foundations of these techniques. We consider some examples of applications by highlighting the essential role of these methods. The ever-increasing production of data requires new methodological and technological approaches to meet the challenge of their effective analyzes. A new machine learning approach based on the Unite and Conquer methods will be presented. This intrinsically parallel and scalable technique can be implemented with synchronous or asynchronous communications. Experimental results, demonstrating the interest of the approach for an effective analysis of data in the case of clustering and anomaly detection will be presented.
Extreme Scale Computing
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