AI News, Probabilistic Graphical Models 1: Representation

Probabilistic Graphical Models 1: Representation

About this course: Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other.

They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more.

  • On Thursday, February 21, 2019

Value of Perfect Information - Stanford University

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over ...

Lecture 16: Dynamic Neural Networks for Question Answering

Lecture 16 addresses the question ""Can all NLP tasks be seen as question answering problems?"". Key phrases: Coreference Resolution, Dynamic Memory ...

New Frontiers in Imitation Learning

The ongoing explosion of spatiotemporal tracking data has now made it possible to analyze and model fine-grained behaviors in a wide range of domains.

Neural Representations for Program Analysis and Synthesis

Representing a program as a numerical vector (i.e., neural representation) enables handling discrete programs using continuous optimization approaches, and ...

Pedro Domingos: "The Master Algorithm" | Talks at Google

Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and ...

Programming Line-Rate Routers

A Google TechTalk, 10/5/16, Presented by Anirudh Sivaraman ABSTRACT: The evolution of network routers and switches has been driven primarily by ...

Crystal Visions - Full Documentary about Crystals and Gemstones

Thanks for your Support! Crystals and gemstones have fascinated and accompanied mankind since recorded history and stories go even further back to the ...

Industry mentor overview | MIT 6.172 Performance Engineering of Software Systems, Fall 2010

Industry mentor (MITPOSSE) overview Instructor: Saman Amarasinghe, Charles Leiserson, Eirik Bakke View the complete course: ..

Mapping the Wilderness of Knowledge: The Card Catalog, Past, Present and Future

Panelists discussed the challenges of managing the "firehose of information" that is modern library collections, from using printed catalog cards to the ...