Galactica is a large-languange model for generating research papers, made by meta research

One of these things. It is actually a binomial distribution.

You can phrase the probability at

Past Work

• self play: this is a \(\text{coNP}\) vs \(\text{NP}\) problem: whereas competitive self-play attempts to defend against all strategies, collaborative self-play only needs to find one useful strategy; this doesn’t generalize well because humans are not a partner
• behavior cloning:
• Population Based Training: computational super e

Novelty

• instead, learn a generative model from both simulated agents or human data
• then, sample from this generative model

Notable Methods

Key Figs

New Concepts

Notes

The GARCH model is a model for the heteroskedastic variations where the changes in variance is assumed to be auto correlated: that, though the variance changes, it changes in a predictable manner.

It is especially useful to

GARCH 1,1

Conditional mean:

\begin{equation} y_{t} = x’_{t} \theta + \epsilon_{t} \end{equation}

Then, the epsilon parameter:

\begin{equation} \epsilon_{t} = \sigma_{t}z_{t} \end{equation}

where:

\begin{equation} z_{t} \sim \mathcal{N}(0,1) \end{equation}

and:

conditional variance

\begin{equation} {\sigma_{t}}^{2} = \omega + \lambda {\sigma_{t-1}}^{2} + \beta {\sigma_{t-1}}^{2} \end{equation}

The Gauss’ Law is a principle of electric flux of uniformly distributed electric field along a surface: that, the electric flux through a closed surface is the sum of the electric charge enclosed divided by the permittivity of free space.

That is:

\begin{equation} \oint E \cdot dA = \frac{\sum Q}{\epsilon_{0}} \end{equation}

somewhat motivating Gauss’ Law

Consider a sphere with uniformly distributed charge on its surface. It has surface area \(4 \pi r^{2}\). Given the expression of electric flux and the fact that the origin change is in the center, and the test change is evenly distributed (i.e. \(E\) is held constant):