Inverse Protein Folding Using Deep Bayesian Optimization

no code implementations • 25 May 2023 • Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner

Furthermore, it is challenging to adapt pure generative approaches to other settings, e. g., when constraints exist.

Bayesian Optimization Protein Folding

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

1 code implementation • 23 Mar 2022 • Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson

Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization.

Bayesian Optimization Denoising

A Hierarchical Approach to Scaling Batch Active Search Over Structured Data

1 code implementation • 20 Jul 2020 • Vivek Myers, Peyton Greenside

As modern data sets grow, so does the need to scale active search to large data sets and batch sizes.

Bayesian Optimization

Learning Important Features Through Propagating Activation Differences

14 code implementations • ICML 2017 • Avanti Shrikumar, Peyton Greenside, Anshul Kundaje

Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input.

Interpretable Machine Learning

Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

1 code implementation • 5 May 2016 • Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, Anshul Kundaje

Note: This paper describes an older version of DeepLIFT.