Jensen's inequality
theorem of convex functions
Save the 5 videos on Jensen's inequality to your own pod.
Sign up free to keep building your knowledge base on Jensen's inequality as more episodes are added.
Videos Mentioning Jensen's inequality
Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU)
Lex Fridman
A mathematical principle that allows optimization of the variational lower bound, enabling learning in variational methods where direct likelihood optimization is intractable.
Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 1 - Diffusion
Stanford Online
A mathematical inequality used to derive the Evidence Lower Bound (ELBO) for intractable probability calculations in diffusion models.
Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 4 - Latent Space & Guidance
Stanford Online
A mathematical inequality used to derive the lower bound of the VAE loss function, allowing for computation despite intractable integrals.
Stanford CS229 Machine Learning | Spring 2026 | Lecture 10: GMM (EM), PCA
Stanford Online
Jensen's inequality relates the value of a concave (or convex) function of a random variable to the random variable's expected value. For a concave function, the expected value of the function is less than or equal to the function of the expected value.
Stanford CS229 Machine Learning | Spring 2026 | Lecture 9: K-Means and GMM (non-EM)
Stanford Online
A mathematical inequality relating the value of a convex function of a random variable to the convex function of the expected value of the random variable. Used to derive the EM algorithm.