Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.

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 ...

Graphical models form a cornerstone of modern data analysis by providing a visually intuitive framework to represent and reason about the complex interdependencies among variables. In particular, ...

We know that correlation does not imply causation, but careful analyses of correlations are often our only way to quantify cause and effect in domains ranging from healthcare to education. This ...

Customers are considering applications for AI inference and want to evaluate multiple inference accelerators. As we discussed last month, TOPS do NOT correlate with inference throughput and you should ...

As AI workloads move from centralised training to distributed inference, the industry’s fibre infra challenge is changing ...

As AI demand shifts from training to inference, decentralized networks emerge as a complementary layer for idle consumer hardware.