Practical Bayesian Methods for Big Data (2019 Workshop)
Event Information
Event description
Description
Join us for the IBM Research workshop on Practical Bayesian Methods for Big Data, scheduled during AI Research Week in Cambridge, Massachusetts!
Bayesian methods have long benefited from their ability to both coherently represent uncertainty and incorporate prior knowledge, but have traditionally struggled to scale to both large data and large models. Deep learning approaches empirically demonstrate the benefits of learning large over-parameterized models from large data, but struggle with producing well calibrated uncertainties. Research attempting to both scale up Bayesian methods and combine the benefits of either paradigm has recently garnered significant attention. Examples include deep generative models and Bayesian neural networks. This workshop will advance and accelerate research on statistical underpinnings of methods at this intersection, including recent advances in Bayesian approaches for learning neural network based models , deep learning methods for Bayesian modeling, methods for scaling up Bayesian inference to large models and data, and use of classical statistical tools for measuring robustness and reliability of deep learning models.
For more information about the Practical Bayesian Methods for Big Data workshop, please visit: https://deeplybayesian.github.io/
Practical Bayesian Methods for Big Data is a workshop held by IBM and MIT as part of the MIT-IBM Watson AI Lab. It is sponsored by the AI Systems research community.
Questions? Contact Nghia Hoang at nghiaht@ibm.com.
To Register, please click on the Register button (above).
Organizer MIT-IBM Watson AI Lab
Organizer of Practical Bayesian Methods for Big Data (2019 Workshop)
