NTR webinar: Catalysis Clustering with GAN by Incorporating Domain Knowledge

NTR organizes and hosts scientific webinars on neural networks and invites speakers from all over the world to present their recent work at the webinars.

On February 9 Olga Andreeva, PhD student, applied ML, University of Massachusetts, Boston, led a technical Zoom webinar on Catalysis Clustering with GAN by Incorporating Domain Knowledge. 

About the webinar: 

Clustering is an important unsupervised learning method with serious challenges when data is sparse and high-dimensional. 

Generated clusters are often evaluated with general measures, which may not be meaningful or useful for practical applications and domains.  In many real-world applications, the number of dimensions is high and data space becomes very sparse.  

Selection of a suitable distance metric is very difficult and becomes even harder when categorical data is involved. Moreover, existing distance metrics are mostly generic, and clusters created based on them will not necessarily make sense to domain-specific applications.  

One option to address these challenges is to integrate domain-defined rules and guidelines into the clustering process. A GAN-based approach called Catalysis Clustering can be used to incorporate domain knowledge into the clustering process.

Moderator and contact:NTR CEO Nick Mikhailovsky: nickm@ntrlab.com.

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