SubLign- A Deep Generative Model for Clustering Censored Time-Series Data
Offered By: Fields Institute via YouTube
Course Description
Overview
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.
Syllabus
Introduction
Challenges
Key Idea
Deep Genetic Models
Case study
Conclusion
Taught by
Fields Institute
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent