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Presenting Variational Transformer Networks, a new approach for automated document layout design that leverages self-attention to develop design rule distributions given a set of examples for a particular task. Read all about it at https://t.co/M9UBXiOY9v
Introducing a simple one-stage approach for automated text-to-image generation that achieves state-of-the-art performance on #LocalizedNarratives using only image-text pairs — no segmentation or bounding box data needed. Learn how it’s done below: https://t.co/SPMbJaVvAN
We're introducing Dynaboard, an evaluation-as-a-service platform for conducting comprehensive evaluations of NLP models. https://t.co/zhLk5zv70J. It enables dynamic, apples-to-apples comparisons dynamically, overcoming some of the key challenges in AI evaluation today.
MUM has the potential to transform how Google helps you with complex tasks. Like BERT, MUM is built on a Transformer architecture, but it’s 1,000 times more powerful. MUM not only understands language, but also generates #googleai#GoogleIO2021 https://t.co/TBwoCkPipW
We’re getting an exclusive first look inside of the @Google#QuantumAI campus.
Tour the campus and learn about qubits and cryostats. We’ll top things off with the journey to building a useful quantum computer.
Come along → https://t.co/yK9G4ZAr7j
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. https://t.co/rppy6u1zcn