Evidential Deep Learning and Uncertainty
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore evidential deep learning and uncertainty estimation in this comprehensive lecture from MIT's Introduction to Deep Learning course. Delve into probabilistic learning, distinguishing between discrete and continuous target learning, and understanding the difference between likelihood and confidence. Examine various types of uncertainty, including aleatoric and epistemic, and learn about Bayesian neural networks. Discover advanced techniques beyond sampling for uncertainty estimation, with a focus on evidential deep learning for both regression and classification tasks. Gain insights into evidential model training and its practical applications. Compare different approaches to uncertainty estimation and grasp the importance of these concepts in the field of deep learning.