#032- Simon Kornblith / GoogleAI - SimCLR and Paper Haul!

This week Dr. Tim Scarfe, Sayak Paul and Yannic Kilcher speak with Dr. Simon Kornblith from Google Brain (Ph.D from MIT). Simon is trying to understand how neural nets do what they do. Simon was the second author on the seminal Google AI SimCLR paper. We also cover "Do Wide and Deep Networks learn the same things?", "Whats in a Loss function for Image Classification?",  and "Big Self-supervised models are strong semi-supervised learners". Simon used to be a neuroscientist and also gives us the story of his unique journey into ML. 00:00:00 Show Teaser / or "short version" 00:18:34 Show intro 00:22:11 Relationship between neuroscience and machine learning 00:29:28 Similarity analysis and evolution of representations in Neural Networks 00:39:55 Expressability of NNs 00:42:33 Whats in a loss function for image classification 00:46:52 Loss function implications for transfer learning 00:50:44 SimCLR paper  01:00:19 Contrast SimCLR to BYOL 01:01:43 Data augmentation 01:06:35 Universality of image representations 01:09:25 Universality of augmentations 01:23:04 GPT-3 01:25:09 GANs for data augmentation?? 01:26:50 Julia language @skornblith https://www.linkedin.com/in/simon-kornblith-54b2033a/ https://arxiv.org/abs/2010.15327 Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth https://arxiv.org/abs/2010.16402 What's in a Loss Function for Image Classification? https://arxiv.org/abs/2002.05709 A Simple Framework for Contrastive Learning of Visual Representations https://arxiv.org/abs/2006.10029 Big Self-Supervised Models are Strong Semi-Supervised Learners