This page shows the most relevant public items for VQ-VAE, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Nov 2, 2017 • arXiv • Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu
Learning useful representations without supervision remains a key challenge in machine learning. We propose the Vector Quantised-Variational AutoEncoder (VQ-VAE), a simple yet powerful generative m...
Paper • Apr 30, 2020 • arXiv • Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever
We introduce Jukebox, a generative model that produces high-fidelity, highly diverse music with singing in the raw audio domain. We model music as a sequence of discrete tokens by using a multi-sca...
It ranks public content for VQ-VAE using recent discussion, review, and engagement signals so you can triage faster. This guidance is specific to VQ-VAE topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use weekly for fast-moving updates, monthly for stable trend confirmation, and all-time for evergreen references. This guidance is specific to VQ-VAE topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
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Yes. Use the follow button on this page to subscribe and track new high-signal activity. This guidance is specific to VQ-VAE topic page on Attendemia and is written so it still makes sense without reading other sections on the page.