my paper on performing with pre-trained generative music models was accepted for oral presentation at #NeurIPS2018 creativity workshop! 🎹🤖🎶🤘🏾 paper: github.com/psc-g/Psc2/blo… code: github.com/psc-g/Psc2/tre… here's a demo based on @herbiehancock 's chameleon 1/3
A lot of PyTorch-powered entries at the top of the DAWNBench deep learning benchmark, which measures training speed, inference latency and cost. dawn.cs.stanford.edu/benchmark/
The BigGAN generators from our paper arxiv.org/abs/1809.11096 are now available on TF Hub (tfhub.dev/s?q=biggan). Try the Colab demo at: colab.research.google.com/github/tensorf…
The final video of my 10 week reinforcement learning course titled "Move 37" is here! I'll explain how we can use what we learned from Move 37 to advance healthcare, design, and decision-making youtube.com/watch?v=vI9Bll…
youtube.com
YouTube
Move 37 Explained
Introducing deepfloat, an approach from @facebookai to make floating point math highly efficient and optimized for AI hardware. Blog post: code.fb.com/ai-research/fl… GitHub: github.com/facebookresear…
We're open sourcing FBGEMM, a low-precision and high-performance kernel library. FBGEMM is optimized for server-side inference, and internally it’s helped us deliver more than 2x performance gains. #opensource code.fb.com/ml-application…
engineering.fb.com
Open-sourcing FBGEMM for state-of-the-art server-side inference
Facebook is open-sourcing FBGEMM, a high-performance kernel library, optimized for server-side inference. Unlike other commonly available libraries, FBGEMM offers optimized on-CPU performance for r…
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