Stanford CS236: Deep Generative Models I 2023 I Lecture 3 - Autoregressive Models

For more information about Stanford's Artificial Intelligence programs visit: stanford.io/ai
To follow along with the course, visit the course website:
deepgenerativemodels.github.io/
Stefano Ermon
Associate Professor of Computer Science, Stanford University
cs.stanford.edu/~ermon/
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[@harshitmeena1625]

@stanfordonline please make all cs / ML / AI courses / maths courses accessable to everyone through this channel , it would really help a lot of people who want to learn about thes subject . please make atleast AI / CS courses avialable

[@CPTSMONSTER]

26:30 Parameters time complexity without reuse 34:20? Invert x 35:15 Parameters time complexity with reuse of weights w 43:40? Bayesian network probability table trained on infinite data would, in principle, be able to capture any relationship 44:00? Difference between lhs and rhs 49:00 Parameterise continuous rv with K Gaussians 53:40 Autoencoders as non linear pca (unsupervised) 56:30 Enforce ordering so that autoencoders can generate samples 1:01:30? Mask weights to get parameters in one pass