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Tranception combines large autoregressive transformers with inference-time retrieval to achieve SOTA performance on protein fitness …

We introduce RITA, a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters

We use the epistemic uncertainty of the VAE decoder to guide the optimization of properties of high-dimensional structured objects …

We leverage deep generative models of evolutionary sequences to predict viral escape mutations.

We introduce an importance sampling-based estimator to estimate the epistemic uncertainty of deep learning models for high-dimensional …