A genetic algorithm is a search heuristic that is inspired by Charles Darwin’s theory of natural evolution.

Its what Grey’s video says. The picking and chucking iterative thing.

Genetic Policy Search involves performing Local Policy Search, but starting from a plurality of initial policies and perturbing the top-k most successful ones (called the “elite samples” \(m_{elite}\)) to generate the next set of starting points.

GenSLMs are a LLM, but genome sequence

1. Take genome sequence
2. Throw transformers at it
3. create “semantic embedding”
4. autoregression happens

This is trained as a foundational model to organize the genomic sequence

Turns out, the embedding space above can be used to discover relations. See proteins can be encoded as hierarchies

GenSLMs are a LLM, but genome sequence

1. Take genome sequence
2. Throw transformers at it
3. create “semantic embedding”
4. autoregression happens

This is trained as a foundational model to organize the genomic sequence

Turns out, the embedding space above can be used to discover relations. See proteins can be encoded as hierarchies

A Geometric Brownian Motion is a Brownian Motion with a drift.

It is determined by:

\begin{equation} \dd{S_{t}} = \mu S_{t} \dd{t} + \sigma \dd{S_{t}} \dd{W_{t}} \end{equation}

where, \(S_{t}\) is a Geometric Brownian Motion, \(\mu\) is its drift, \(\sigma\) the volatility, and \(W_{t}\) a centered Brownian Motion.