[ML News] GPT-3 learns to edit | Google Pathways | Make-A-Scene | CLIP meets GamePhysics | DouBlind
#mlnews #gpt3 #pathways
Your updates on the latest and greatest from the depths of Machine Learning!
Sponsor: Weights & Biases
https://wandb.me/yannic
OUTLINE:
0:00 – Intro
0:15 – Weights & Biases Report about Reports
2:45 – GPT-3 learns to edit
6:30 – Make-A-Scene: Text-to-Image with Human Priors
8:00 – Pathways: Google’s new High-Performance ML scheduler
10:45 – DouBlind: Open Peer-Review
12:45 – CLIP meets GamePhysics
14:40 – Residual Quantization pushes Image Generation SOTA
16:15 – Helpful Things
References:
Weights & Biases Report about Reports
https://wandb.ai/wandb/wandb_example/reports/How-many-discoveries-were-lost-because-they-weren-t-written-down—VmlldzoxMjY3MDk5
GPT-3 learns to edit
https://openai.com/blog/gpt-3-edit-insert/?utm_source=pocket_mylist
https://beta.openai.com/playground?model=code-davinci-002
Make-A-Scene: Text-to-Image with Human Priors
https://arxiv.org/pdf/2203.13131.pdf
Pathways: Google’s new High-Performance ML scheduler
https://arxiv.org/pdf/2203.12533.pdf
DouBlind: Open Peer-Review
https://doublind.com/#web-intro
https://doublind.com/search?query=kilcher
CLIP meets GamePhysics
https://arxiv.org/pdf/2203.11096.pdf
[Red Dead Redemption 2] Things you find in RDR2 from GamePhysics
https://asgaardlab.github.io/CLIPxGamePhysics/
Residual Quantization pushes Image Generation SOTA
https://arxiv.org/pdf/2203.01941.pdf
https://github.com/kakaobrain/rq-vae-transformer
Helpful Things
https://github.com/TDAmeritrade/stumpy
https://github.com/linkedin/fasttreeshap
https://github.com/vopani/jaxton
Introducing NovGrid: a testbed for experimenting with RL agents facing novel environmental changeshttps://t.co/P6q45hWvuvhttps://t.co/fQy7iA6xkI
Novelty is a sudden change to the world dynamics at test time, after an agent’s policy has converged and learning is turned off. pic.twitter.com/cmEnWaJ9YX
— Mark Riedl (@mark_riedl) March 25, 2022
https://github.com/eilab-gt/NovGrid
https://developer.nvidia.com/isaac-gym
https://github.com/NVIDIA-Omniverse/IsaacGymEnvs
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