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 ...

If you already have an account please use the link below to sign in. If you have any problems with your access or would like to request an individual access account please contact our customer service ...

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher ...

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 ...