As the first author of the article Peter Schiansky says “Remarkably, this protocol does not even require the nature of the interactions with the quantum system to be known”
https://t.co/kf9BV8mnaN
Exploring in sparse-reward partially-observable tasks? BYOL-Explore harnesses the power of Self-Supervised Learning to solve such tasks only with prediction error as intrinsic reward, producing a conceptually simple yet powerful exploration agent: https://t.co/2M84ufghAP 1/
It’s been a month and we’ve already seen some amazing examples of scientists using #AlphaFold in their research. If AlphaFold has been beneficial to your work, we’d love to hear about it. Please share your stories with us via [email protected]!
Interested in playing around with RL? We’re happy to announce the release of Acme, a light-weight framework for building and running novel RL algorithms.
We also include a range of pre-built, state-of-the-art agents to get you started. Enjoy! https://t.co/2wzpK3nCKp
We helped BERT do better on several structured prediction tasks by learning from a language model that knows more about syntax, showcasing the benefits of structural biases in large-scale models.
Read about it here: https://t.co/zm418xTGhp