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Graphical model, Graph theory, Bayesian probability, Probability theory, Bayesian network, Learning

High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions

On Sep 27, 2020
@AnimaAnandkumar shared
Seeing many first-time #NeurIPS2020 co-authors on my team at @NVIDIAAI @Caltech makes me nostalgic. My first #neurips paper was my first ever submission to #AI venue a decade ago and it got an oral! Couldn't hope for a warmer welcome to #AI community! https://t.co/PHqudn9ScO https://t.co/JUeQzliKXN
Open

The class of graphs is based on a local-separation property and includes many well-known random graph families, including locally-tree like graphs such as large girth graphs, the Erdo˝s-Re´nyi random graphs [7] and power-law graphs [8], as well as graphs with short cycles such as ...

papers.nips.cc
On Sep 27, 2020
@AnimaAnandkumar shared
Seeing many first-time #NeurIPS2020 co-authors on my team at @NVIDIAAI @Caltech makes me nostalgic. My first #neurips paper was my first ever submission to #AI venue a decade ago and it got an oral! Couldn't hope for a warmer welcome to #AI community! https://t.co/PHqudn9ScO https://t.co/JUeQzliKXN
Open

High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions

High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions

The class of graphs is based on a local-separation property and includes many well-known random graph families, including locally-tree like graphs such as large girth graphs, the ...

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Bayesian Structure Learning by Recursive Bootstrap Raanan Y. Rohekar∗ Intel AI Lab [email protected] Yaniv Gurwicz∗ Intel AI Lab [email protected] Shami Nisimov∗ Intel AI Lab ...

Belief propagation

Belief propagation

Belief propagation is commonly used in artificial intelligence and information theory and has demonstrated empirical success in numerous applications including low-density parity-check ...