Self-Supervised Learning


36:04 [Talk: Self-Supervised Learning by Yann LeCun] 36:25 What is Deep Learning? 38:37 Supervised Learning works but requires many labeled samples 39:25 Supervised DL works amazingly well, when you have data 40:05 Supervised Symbol Manipulation 41:50 Deep Learning Saves Lives 43:40 Reinforcement Learning: works great for games and simulations. 45:12 Three challenges for Deep Learning 47:39 How do humans and animals learn so quickly? 47:43 Babies learn how the world works by observation 48:43 Early Conceptual Acquisition in Infants [from Emmanuel Dupoux] 49:33 Prediction is the essence of Intelligence 50:28 Self-Supervised Learning = Filling in the Blanks 50:53 Natural Language Processing: works great! 51:55 Self-Supervised Learning for Video Prediction 52:09 The world is stochastic 52:43 Solution: latent variable energy-based models 53:55 Self-supervised Adversarial Learning for Video Prediction 54:12 Three Types of Learning 55:30 How Much Information is the Machine Given during Learning? 55:54 The Next Al Revolution 56:23 Energy-Based Models 56:32 Seven Strategies to Shape the Energy Function 57:02 Denoising AE: discrete 58:44 Contrastive Embedding 1:00:39 MoCo on ImageNet 1:00:52 Latent-Variable EBM for inference & multimodal prediction 1:02:07 Learning a (stochastic) Forward Model for Autonomous Driving 1:02:26 A Forward Model of the World 1:04:42 Overhead camera on highway. Vehicles are tracked 1:05:00 Video Prediction: inference 1:05:15 Video Prediction: training 1:05:30 Actual, Deterministic, VAE+Dropout Predictor/encoder 1:05:57 Adding an Uncertainty Cost (doesn’t work without it) 1:06:01 Driving an Invisible Car in “Real” Traffic 1:06:51 Conclusions

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