MIT Introduction to Deep Learning 6.S191: Lecture 7 Evidential Deep Learning and Uncertainty Estimation Lecturer: Alexander Amini January 2021
For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline 0:00 – Introduction and motivation 5:00 – Outline for lecture 5:50 – Probabilistic learning 8:33 – Discrete vs continuous target learning 14:12 – Likelihood vs confidence 17:40 – Types of uncertainty 21:15 – Aleatoric vs epistemic uncertainty 22:35 – Bayesian neural networks 28:55 – Beyond sampling for uncertainty 31:40 – Evidential deep learning 33:29 – Evidential learning for regression and classification 42:05 – Evidential model and training 45:06 – Applications of evidential learning 46:25 – Comparison of uncertainty estimation approaches 47:47 – Conclusion