This repository contains both MATLAB and R code for implementing the Bayesian GP-LVM. The MATLAB code is in the subdirectory vargplvm, the R code in vargplvmR. For a quick description and sample videos / demos check: http://git.io/A3Uv The Bayesian GP-LVM (Titsias and Lawrence, 2010) is an extension of the traditional GP-LVM where the latent space is approximately marginalised out in a variational fashion (hence the prefix'vargplvm'). Let us denote \mathbf{Y} as a matrix of observations (here called outputs) with dimensions n \times p, where n rows correspond to datapoints and p columns to dimensions.

Since we released our text summarization resources, the legal technology community has shown interest in leveraging summarization technology to support litigation document review, deposition digests, and contract analysis. Data scientist interest to use machine learning to mine legal document corpuses and support legal strategy was also one factor motivating our summarization research. The time is therefore ripe for data scientists to apply new text analytics capabilities for legal use cases. But to be effective, data scientists must first understand how lawyers think: what problems they're trying to solve, how their processes are structured, and, perhaps most importantly, what fears may hinder the adoption of new technologies. This guest blog post from Dean Gonsowski, kCura's VP of Business Development, provides tips to help data scientists explain the value of machine learning to lawyers. Electronic discovery software helps manage the exchange of electronically stored information, documents that could be used as evidence in various forms of litigation like investigations or contract reviews.

RE•WORK will host it's annual East Coast events on Deep Learning and the Internet of Things in Boston on 12 & 13 May. Over 300 machine learning and IoT enthusiasts and experts will come together to hear keynote presentations, panel discussions, fireside chats and to explore the startup showcase area. The Deep Learning Summit brings together leaders from industry, academia and startups to explore advances in deep learning methods and techniques, as well as their business applications in areas including finance, manufacturing, healthcare & transportation. Professor Bengio is also Head of the Machine Learning Laboratory, Co-director of the CIFAR Neural Computation & Adaptive Perception program, and editor of many prestigious machine learning publications. Daniel's work has been reported on in The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine.

"What is the ape to a man? And so shall man be to the Übermensch." Years ago, as a teenager in France, I used to visit a family friend, Monsieur de la Place. He lived with his wife in a high-rise in a small town outside of Paris. An old man, one side of his body had been paralyzed by a stroke. He was very educated and profound, and became a mentor of sorts to me for a time.

The trucking industry has a driver problem. The job pays well and doesn't require a college degree, but the long hours and lonely stretches on the road make it a tough way of life. That's why the long haul trucking market sees an annual labor turnover rate of more than 90 percent, according to Bob Costello, chief economist at the American Trucking Association. The industry estimates it will need to hire a total of 890,000 new truck drivers over the next decade. The driver shortage is a problem for everyone, because trucking's crucial to the American economy.

Machine translation (MT) systems such as Google Translate have become part of our daily life. But how do they work? In this talk, I'll explain how these systems are built. In the first part of my talk, I'll present a general overview of the field and the key ideas driving modern MT systems. In the second part, I'll dig deeper into the statistical techniques used to estimate translation models from data, and discuss some of the current hot topics in the field."

The "rise of the machines" may not be so far after all as Hitachi appointed its first Artificial Intelligence manager back in September 2015. Isaac Asimov (Late American author and professor of biochemistry at Boston University,) coined the term robotics in his 1950 classic I, Robot. The Japanese Multinational conglomerate enthroned a robot as a supervisor to induce more efficiency and productivity. The golem chief proved to be efficient, as 8% increase in efficiency was noticed. Lately, Airbus has announced the commencement of a program dedicated to the research and development of humanoid robotic technology to carry out intricate manufacturing errands in factories.

Since the University of Amsterdam is slowly merging their science faculty with the one of the Free University(VU) (the other major uni of Amsterdam), some classes are given at VU. These tend to be easier, going less in-depth from what I heard and are more focused on knowledge representation and computational intelligence and symbolic AI in general. I believe two or so are mandatory to take.

The idea of enhancing an image digitally to reveal previously unseen levels of detail has become something of a sci-fi movie cliché. Now, astronomers at University College London have used a similar computer vision technique to learn more about the location of the missing Beagle 2 probe on the surface of Mars. A paper on the novel "stacking and matching" technique, which allows researchers to pick out objects at a resolution of up to five times greater than the original images, was published in the journal Planetary and Space Science in February. But just recently, the UCL researchers began using the technique, called Super-Resolution Restoration (SRR), to identify individual objects on the red planet's surface. "We now have the equivalent of drone-eye vision anywhere on the surface of Mars where there are enough clear repeat pictures," Jan-Peter Muller of UCL's Mullard Space Science Laboratory says in a press release.

BRUSSELS--Getty Images Inc., the world's largest photo agency, said Wednesday it has lodged an official complaint with the European Union's antitrust watchdog over Alphabet Inc.'s Google, accusing the U.S. technology giant of abusing its dominance in search to display copied images without compensation. The fresh complaint comes after the European Commission filed a new set of formal charges against Google last week for abusing its dominance with its Android mobile operating system to protect its search engine.