Deep Mind announced that its agent beated human performance in Quake III CTF (Capture The Flag)

https://deepmind.com/blog/capture-the-flag/

#rl #quake3 #deepmind

Neural scene representation and rendering

In June #DeepMind introduced Generative Query Network (#GQN) framework within which machines learn to perceive their surroundings by training only on data obtained by themselves as they move around scenes.

Link: https://deepmind.com/blog/neural-scene-representation-and-rendering/

#DeepMind new release: Neural Processes (#NPs) that generalise #GQN ’s training regime to other few-shot prediction tasks such as regression and classification

Arxiv 1: https://arxiv.org/abs/1807.01622
Arxiv 2: https://arxiv.org/abs/1807.01613

#ICML2018

Teams at #DeepMind and #Moorfields have developed AI technology that can detect eye disease and prioritise patients. 'Clinically applicable deep learning for diagnosis and referral in retinal OCT' has been published online in #NatureMedicine today:
https://www.nature.com/articles/s41591-018-0107-6

#cv #dl

Neural nets are terrible at arithmetic & counting. If you train one in 1 to 10, it will do okay on 3 + 5 but fail miserably for 1000 + 3000. Resolving this, «Neural Arithmetic Logic Units» can track time, do arithmetic on images of numbers, & extrapolate, providing better results than other architectures.

https://arxiv.org/pdf/1808.00508.pdf

#nn #architecture #concept #deepmind #arithmetic

Paper «A Probabilistic U-Net for Segmentation of Ambiguous Images» from #NIPS2018 spotlight presentation.

Github: https://github.com/SimonKohl/probabilistic_unet
Github: Arxiv: https://arxiv.org/abs/1806.05034

#DeepMind #segmentation #cv

#DeepMind ’s library for deep learning on graphs.


ArXiV: https://arxiv.org/abs/1806.01261
Github: https://github.com/deepmind/graph_nets

🎓 Free «Advanced Deep Learning and Reinforcement Learning» course.

#DeepMind researchers have released video recordings of lectures from «Advanced Deep Learning and Reinforcement Learning» a course on deep RL taught at #UCL earlier this year.

YouTube Playlist: https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs

#course #video #RL #DL

​​🔥 AlphaFold: Using AI for scientific discovery.

#DeepMind has significally improved protein folding prediction.

Protein folding is important because it allows to predict function along with the functioning mechanism.

Website: https://deepmind.com/blog/alphafold/
Guardian: https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins

#bioinformatics #alphafold #genetics

​​Papers from #DeepMind panel at #NIPS2018

Work on radiotherapy planning: https://arxiv.org/abs/1809.04430
Triaging eye diseases: https://www.nature.com/articles/s41591-018-0107-6
Probabilistic U-net: https://arxiv.org/abs/1806.05034

#segmentation #CV #Unet

#DeepMind will show AI playing #Starcraft II.

Starts in 8 hours (6:00 PM GMT)

youtube.com/c/deepmind / https://www.twitch.tv/starcraft

#RL

​​Large Scale Adversarial Representation Learning

DeepMind shows that GANs can be harnessed for unsupervised representation learning, with state-of-the-art results on ImageNet. Reconstructions, as shown in paper, tend to emphasise high-level semantics over pixel-level details.

Link: https://arxiv.org/abs/1907.02544

#DeepMind #GAN #CV #DL #SOTA

DeepMind's Behaviour Suite for Reinforcement Learning

DeepMind released Behaviour Suite for Reinforcement Learning, or ‘bsuite’ – a collection of carefully-designed experiments that investigate core capabilities of RL agents.

bsuite was built to do two things:

1. Offer clear, informative, and scalable experiments that capture key issues in RL
2. Study agent behaviour through performance on shared benchmarks

GitHub: https://github.com/deepmind/bsuite
Paper: https://arxiv.org/abs/1908.03568v1
Google colab: https://colab.research.google.com/drive/1rU20zJ281sZuMD1DHbsODFr1DbASL0RH

#RL #DeepMind #Bsuite