**GPT-4** is a transformer based model pre-trained to predict the next token in a document.

Given a training set drawn from an unknown $d$-variate probability distribution, QuantTree constructs a histogram by recursively splitting $\mathbb{R}^d$. The splits are defined by a stochastic process so that each bin contains a certain proportion of the training set. These histograms can be used to define test statistics (e.g., the Pearson statistic) to tell whether a batch of data is drawn from $\phi_0$ or not. The most crucial property of QuantTree is that the distribution of any statistic based on QuantTree histograms is independent of $\phi_0$, thus enabling nonparametric statistical testing.

QuantTree

QuantTree: Histograms for Change Detection in Multivariate Data Streams

GPT-4

GPT-4 Technical Report

**MADDPG**, or **Multi-agent DDPG**, extends [DDPG](https://paperswithcode.com/method/ddpg) into a multi-agent policy gradient algorithm where decentralized agents learn a centralized critic based on the observations and actions of all agents. It leads to learned policies that only use local information (i.e. their own observations) at execution time, does not assume a differentiable model of the environment dynamics or any particular structure on the communication method between agents, and is applicable not only to cooperative interaction but to competitive or mixed interaction involving both physical and communicative behavior. The critic is augmented with extra information about the policies of other agents, while the actor only has access to local information. After training is completed, only the local actors are used at execution phase, acting in a decentralized manner.

Source	GPT-4 Technical Report
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com