A five-minute formula from Alexander Denev that takes you through a simple probabilistic graphical model and explains how and why these are used. Find out more about the ground-breaking book, ...

Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how ...

A recent announcement appearing in MIT News, “Machine learning branches out,” highlights new research in probabilistic graphical models. In a paper being presented in December at the annual conference ...

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Many AI applications work with data in the form of tensors or with models including tensor structures. Here, tensor decompositions are highly effective to efficiently handle large tensors, ...

Over the past two decades, new technologies have helped scientists generate a vast amount of biological data. Large-scale experiments in genomics, transcriptomics, proteomics, and cytometry can ...

Many scientific studies involve comparing multiple datasets collected under different conditions to identify the difference in the underlying distributions. A common challenge in these multi-sample ...