Learning Data Augmentations
Daniel and Alex discuss the challenges of selecting data augmentations and propose using a neural network to learn and generate diverse augmentations for images. They explore the idea of training the network adversarially to produce distinct images that are difficult to match together.In this clip
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The Gradient
Alex Tamkin on Self-Supervised Learning and Large Language Models
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