Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

Numerical analysis develops, analyses and implements algorithms for approximating the solutions of mathematical problems that cannot be expressed in closed form. Central challenges include ...

According to National Research Council (NRC) Canada, at the Automotive and Surface Transportation Research Centre, researchers are using advanced numerical simulation to detect potential manufacturing ...

This set of tutorials are written at an introductory level for an engineering or physical sciences major. It is ideal for someone who has completed college level courses in linear algebra, calculus ...

The elemental imaging of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) provides spatial information on elements and therefore can further investigate the growth or evolution ...

The bleeding edge: In-memory processing is a fascinating concept for a new computer architecture that can compute operations within the system's memory. While hardware accommodating this type of ...

Dr. James McCaffrey of Microsoft Research says that when quantum computing becomes generally available, evolutionary algorithms for training huge neural networks could become a very important and ...

Ask the publishers to restore access to 500,000+ books. An icon used to represent a menu that can be toggled by interacting with this icon. A line drawing of the Internet Archive headquarters building ...

Private methods are often used as an implementation detail and are not meant to be accessed directly by the users of a class. The name mangling mechanism in Python makes it difficult to call private ...