SubLign- A Deep Generative Model for Clustering Censored Time-Series Data

Explore a deep generative model for clustering censored time-series data in this 30-minute seminar presented by Rahul Krishnan from the University of Toronto. Delve into the challenges, key ideas, and deep genetic models associated with SubLign, a novel approach to analyzing complex temporal data. Gain insights from a relevant case study and understand the potential applications of this innovative technique in the field of machine learning. Part of the 2021-2022 Machine Learning Advances and Applications Seminar series at the Fields Institute, this talk offers a comprehensive overview of cutting-edge research in time-series data analysis.

Introduction
Challenges
Key Idea
Deep Genetic Models
Case study
Conclusion