Previous proposalGendered Biases in Artificial Intelligence
How we applied sampling algorithms to extract meaning from data (@ Belong.co)
Submitted by Vinodh Kumar Ravindranath (@vinodh-kumar) on Thursday, 7 November 2019
Session type: Full talk of 40 mins Status: Confirmed & Scheduled
A lot of unsupervised learning algorithms work by inferencing parameters of generative models through Monte Carlo techniques. In this talk, we will go into details of the underlying inference algorithms that use sampling techniques and then proceed step-by-step applying it to couple of real world problems, particularly some of our work at Belong that we recently published at ICDAR‘19. The attendees, in addition to learning inferencing algorithms (such as Gibbs sampling) and various probabilistic models (Dirichlet, Wishart distributions, etc), will get a glimpse of how to model real world problems, apply ML algorithms and work through the practical challenges that are encountered while building a high quality data science product / solution.
Generative models => Basic idea of sampling algorithms to inference parameters => Simple example using Gibbs sampling => Application to a more complex problem of resume understanding at Belong.co
Currently CTO at Belong.co, Vinodh Kumar is one of the top industry leaders with more than a decade of hands-on experience in search, ranking and machine learning. Prior to Belong, Vinodh used to be CTO/M.D of Bloomreach driving their e-commerce search engine efforts. Earlier Vinodh spent more than 6 years at Google leading the Google News team and building the ranking algorithms that power Google News. He did his masters in computer science from the Indian Institute of Science after securing the All India Rank #1 in Graduate Engineering Entrance Exam (GATE ‘99) in computer science. He has more than 10 patents to his name.