Explore tweets tagged as #VariationalSynthesis
So pleased our work on #VariationalSynthesis was selected as one of the top 4 papers at @AIHealthMIT #MoML2024. @EliWeinstein6 will be giving a short talk in person if you'd like to learn more live.
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This Thursday, @EliWeinstein6 is traveling to the University of Chicago to give a joint Statistics/Data Science colloquium. If you've been interested in learning more about #variationalsynthesis and are local, I encourage you to attend!
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Congratulations to the whole team on the release of #variationalsynthesis today. 🏎️.
I’m thrilled to announce: at @jura_bio we’ve constructed a new kind of generative protein model, which allows us to manufacture its generated designs in the real world at petascale. Paper: Blog:
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*Manufacturing-aware generative model architectures*; By JURA Bio, Inc. The "Variational Synthesis" model is made so that designed sequences can be efficiently synthesized in the real world with extreme parallelism.
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I’m thrilled to announce: at @jura_bio we’ve constructed a new kind of generative protein model, which allows us to manufacture its generated designs in the real world at petascale. Paper: Blog:
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Tune in for our CEO @lizbwood's talk on foundational models in biology & the role of #VariationalSynthesis scaled data as part of @GENbio's The State of AI in Drug Discovery virtual summit on Oct. 30, organized by @xiaofei_lin. Register for free ✍️
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Imagine building trillions of designed proteins at a lower cost than the GPU used to predict them. Antibodies? Your favorite enzyme? . At @jurabio we built #variationalsynthesis to sample from generative models at synthesis time - at petascale!.
I’m thrilled to announce: at @jura_bio we’ve constructed a new kind of generative protein model, which allows us to manufacture its generated designs in the real world at petascale. Paper: Blog:
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You can register now for free at to listen to @jura_bio CEO @lizbwood speak to the possibilities of foundation models in biology. She puts #variationalsynthesis as a tool for building incomparably large data sets for training these models into context.
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