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.

The geometric multplicity for a given eigenvalue is the dimension of its generated eigenspace.