Summary of Variational Search Distributions, by Daniel M. Steinberg et al.
Variational Search Distributions
by Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong, Edwin V. Bonilla
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed VSD method efficiently conditions a generative model to produce discrete, combinatorial designs with rare desired classes. By leveraging black-box experiments or simulations in a batch sequential manner, VSD tackles the task of active generation, formalized through variational inference and off-the-shelf gradient-based optimization routines. The approach learns powerful generative models for desirable designs and can take advantage of scalable predictive models. Empirical results demonstrate that VSD outperforms baseline methods on protein and DNA/RNA engineering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VSD is a new way to make computers generate specific designs, like proteins or DNA sequences. It’s like asking a computer to create a certain image, but instead it makes molecules! The method uses existing algorithms and can learn from lots of data. This helps scientists design new molecules that have specific properties. |
Keywords
» Artificial intelligence » Generative model » Inference » Optimization
