MIT 6.S191: Evidential Deep Learning and Uncertainty
Alexander Amini Alexander Amini
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 Published On Premiered Mar 19, 2021

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


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