This repository contains the code for the WiDS lecture "Graph Theory for Data Science, Part III: Characterizing graphs in the real world": Many of the systems we study today can be represented as ...
Network (knowledge) graphs represent a collection of interlinked entities organized into contexts via linking and semantic metadata. They build a framework for data integration and analysis. Having ...
Abstract: Graphical models such as factor graphs allow to model complex systems and help to derive practical detection/estimation algorithms as message passing in the graph. In this paper, we outline ...
Abstract: Expander graphs are highly connected sparse finite graphs. The property of being an expander seems significant in many of these mathematical, computational ...
A popular approach to learning about admixture from population genetic data is by computing the allele-sharing summary statistics known as f-statistics. Compared to some methods in population genetics ...
Attribution Graphs Explorer is a framework for mechanistic interpretability of transformer models based on circuit tracing methods. The toolkit allows researchers to: Extract computational circuits ...
A popular approach to learning about admixture from population genetic data is by computing the allele-sharing summary statistics known as f-statistics. Compared to some methods in population genetics ...
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