Presenting DreamerV2, the first world model-based #ReinforcementLearning agent to achieve top-level performance on the Atari benchmark, learning general representations from images to discover successful behaviors in latent space. Read more at goo.gle/3ub8iZe
Introducing NFNets, a family of image classification models that are:
*SOTA on ImageNet (86.5% top-1 w/o extra data)
*Up to 8.7x faster to train than EfficientNets to a given accuracy
*Normalizer-free (no BatchNorm!)
Paper: dpmd.ai/06171
Code: dpmd.ai/nfnets
We’ve developed two neural networks which have learned by associating text and images. CLIP maps images into categories described in text, and DALL-E creates new images, like this, from text.
A step toward systems with deeper understanding of the world. openai.com/multimodal/