Fast Quantification of Uncertainty and Robustness with Variational Bayes

Fast Quantification of Uncertainty and Robustness with Variational Bayes

Share:

Similar Tracks

Probabilistic Programming and Bayesian Modeling with PyMC3 - Christopher Fonnesbeck Montreal-Python

Tamara Broderick: Variational Bayes and Beyond: Bayesian Inference for Big Data (ICML 2018 tutorial) Steven Van Vaerenbergh

Uncertainty Quantification of Nonlinear Systems APMonitor.com

Mircea Grigoriu - Probability Theory (1 of 2) Liverpool Risk Institute

Yee Whye Teh: On Bayesian Deep Learning and Deep Bayesian Learning (NIPS 2017 Keynote) Steven Van Vaerenbergh

Variational Inference: Foundations and Modern Methods (NIPS 2016 tutorial) Steven Van Vaerenbergh

Nonparametric Bayesian Methods: Models, Algorithms, and Applications II Simons Institute

11. Introduction to Machine Learning MIT OpenCourseWare

Tutorial Session: Variational Bayes and Beyond: Bayesian Inference for Big Data ICML IJCAI ECAI 2018 Conference Videos

History of Bayesian Neural Networks (Keynote talk) Bayesian Deep Learning Workshop NIPS 2016

6. Monte Carlo Simulation MIT OpenCourseWare

Variational Inference | Evidence Lower Bound (ELBO) | Intuition & Visualization Machine Learning & Simulation

Andrew Rowan - Bayesian Deep Learning with Edward (and a trick using Dropout) PyData

Stanford CS229 I Machine Learning I Building Large Language Models (LLMs) Stanford Online

Dave Blei: "Black Box Variational Inference" PROBPROG Conference

Think Fast, Talk Smart: Communication Techniques Stanford Graduate School of Business

Michael Betancourt: Scalable Bayesian Inference with Hamiltonian Monte Carlo London Machine Learning Meetup