NYU Deep Learning SP21 - 05L – Joint embedding method and latent variable energy based models (LV-EBMs)

Course website: http://bit.ly/DLSP21-web Playlist: http://bit.ly/DLSP21-YouTube Speaker: Yann LeCun Chapters 00:00:00 – Welcome to class 00:00:39 – Predictive models 00:02:25 – Multi-output system 00:06:36 – Notation (factor graph) 00:07:41 – The energy function F(x, y) 00:08:53 – Inference 00:11:59 – Implicit function 00:15:53 – Conditional EBM 00:16:24 – Unconditional EBM 00:19:18 – EBM vs. probabilistic models 00:21:33 – Do we need a y at inference? 00:23:29 – When inference is hard 00:25:02 – Joint embeddings 00:28:29 – Latent variables 00:33:54 – Inference with latent variables 00:37:58 – Energies E and F 00:42:35 – Preview on the EBM practicum 00:44:30 – From energy to probabilities 00:50:37 – Examples: K-means and sparse coding 00:53:56 – Limiting the information capacity of the latent variable 00:57:24 – Training EBMs 01:04:02 – Maximum likelihood 01:13:58 – How to pick β? 01:17:28 – Problems with maximum likelihood 01:20:20 – Other types of loss functions 01:26:32 – Generalised margin loss 01:27:22 – General group loss 01:28:26 – Contrastive joint embeddings 01:34:51 – Denoising or mask autoencoder 01:46:14 – Summary and final remarks