| website | https://pemami4911.github.io |
| github | https://github.com/pemami4911 |
Not necessarily! We use gradients to craft an efficient proposal distribution for sampling from high-dimensional and discrete product of experts.
For example, this enables us to do things like maximize the sum of two binary MNIST digits just by flipping binary pixels:
3/
Combining multiple models in sequence space is straightforward if we treat each as one expert in a product of experts, like in energy based models.
"But isn't directed evolution just doing brute force or random search for mutations?” 🤔
2/
New pre-print!
Plug and play generation works for images and text…what about proteins?
We engineer proteins by combining your favorite unsupervised and supervised protein sequence models (even protein language models!) in a fast *gradient-based* discrete MCMC sampler.
🧵
Will be at @workshopmlsb today presenting our poster “Plug and Play Directed Evolution of Proteins with Gradient-Based Discrete MCMC”
We mix and match pre-trained protein models to do search with fast discrete MCMC :)
