The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

CAMBRIDGE, Mass.--(BUSINESS WIRE)--Today, Gamalon, Inc. emerged from stealth mode to announce that it has developed a game-changing new approach to artificial intelligence/machine learning called ...

This illustration gives a sense of how characters from alphabets around the world were replicated through human vs. machine learning. (Credit: Danqing Wang) Researchers say they’ve developed an ...

Even in this day and age, computer learning is far behind the learning capability of humans. A team of researchers seek to shrink the gap, however, developing a technique called “Bayesian Program ...

The entire tech industry has fallen hard for a branch of artificial intelligence called deep learning. Also known as deep neural networks, the AI involves throwing massive amounts of data at a neural ...

This is a preview. Log in through your library . Abstract This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends ...

The old adage that practice makes perfect applies to machines as well, as many of today’s artificially intelligent devices rely on repetition to learn. Deep-learning algorithms are designed to allow ...

We present a spatial Bayesian hierarchical model for seasonal extreme precipitation. At the first level of hierarchy, the seasonal maximum precipitation (i.e. block maxima) at any location is assumed ...