Kernel methods give powerful, flexible, and theoretically grounded approaches to solving many problems in machine learning. The standard approach, however, requires pairwise evaluations of a kernel function, which can lead to scalability issues for very large datasets. Rahimi and Recht (2007) suggested a popular approach to handling this problem, known as random Fourier features. The quality of this approximation, however, is not well understood. We improve the uniform error bound of that paper, as well as giving novel understandings of the embedding's variance, approximation error, and use in some machine learning methods. We also point out that surprisingly, of the two main variants of those features, the more widely used is strictly higher-variance for the Gaussian kernel and has worse bounds.

We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. This learned operator replaces the multivariate integral required in classical EP, which may not have an analytic expression. We use kernel-based regression, which is trained on a set of probability distributions representing the incoming messages, and the associated outgoing messages. The kernel approach has two main advantages: first, it is fast, as it is implemented using a novel two-layer random feature representation of the input message distributions; second, it has principled uncertainty estimates, and can be cheaply updated online, meaning it can request and incorporate new training data when it encounters inputs on which it is uncertain. In experiments, our approach is able to solve learning problems where a single message operator is required for multiple, substantially different data sets (logistic regression for a variety of classification problems), where it is essential to accurately assess uncertainty and to efficiently and robustly update the message operator.

Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation.

Apple on Monday served up a veritable smorgasbord of digital delights for its fans, unveiling at its annual developers conference upgrades to its mobile and desktop software, showing off a gussied-up Siri with a new bag of tricks, and firing a shot over Spotify's bow with its new streaming Apple Music subscription service. "This is a truly revolutionary music service," Eddy Cue, Apple's senior vice president of Internet software and services, told the crowd of several thousand developers, designers and product managers at the 26th Worldwide Developers Conference, the annual Apple love fest at Moscone Center in San Francisco. "Apple Music will bring you all of your music all in one place." Revealed toward the end of a nearly three-hour extravaganza, the music feature was clearly Apple's rabbit out of a hat. It had been widely expected for months, ever since May last year when Apple bought subscription streaming music service Beats Music, and Beats Electronics, which makes the popular Beats headphones, speakers and audio software.

Apple on Monday served up a veritable smorgasbord of digital delights for its fans, unveiling at its annual developers conference upgrades to its mobile and desktop software, showing off a gussied-up Siri with a new bag of tricks, and firing a shot over Spotify's bow with its new streaming Apple Music subscription service. "This is a truly revolutionary music service," Eddy Cue, Apple's senior vice president of Internet software and services, told the crowd of several thousand developers, designers and product managers at the 26th Worldwide Developers Conference, the annual Apple love fest at Moscone Center in San Francisco. "Apple Music will bring you all of your music all in one place." Revealed toward the end of a nearly three-hour extravaganza, the music feature was clearly Apple's rabbit out of a hat. It had been widely expected for months, ever since May last year when Apple bought subscription streaming music service Beats Music, and Beats Electronics, which makes the popular Beats headphones, speakers and audio software.

At Apple's WWDC developers' conference keynote in San Francisco, the company's senior vice president of software engineering, Craig Federighi, highlighted a number of new tools coming to Apple's iOS 9 update. Some of these features include a native news aggregator (aptly called News); a refreshed user interface for the more "proactive" digital voice assistant Siri, and split-screen capabilities for the iPad. But similar to past WWDC events, Apple only spent time going through what it considered key upgrades. The other, less high-profile features were thrown onto a keynote slide and glossed over almost completely. Below are the 30 features Apple did not parse through during the presentation: One notable item on the list is "app thinning," which lets users download apps that are tailored to their iOS device.

A robot that evoked a human form paused in front of a door leading to a simulated nuclear power plant accident and inexplicably stood motionless. Suddenly, from the grandstands overlooking the scene, a group of schoolchildren began to chant: "Go Robot! What has long been thought of as a brave new world in which mobile robots freely move about in factories, towns and cities is now approaching. Robots will advance from the dull, dirty and dangerous work that they do today to take on a range of tasks, from rescue work to elder care in close contact with humans. Just as software robots such as Apple's Siri and Microsoft's Cortana have rapidly become useful personal assistants, physical robots will occupy a place in the near future. That is the world imagined by government officials and technologists at the Defense Advanced Research Projects Agency, the American military organization that is charged with the mission of avoiding a Sputnik-style technology threat to national security. Last weekend at the sprawling Los Angeles County Fairgrounds, Darpa concluded the Robotics Challenge, a two-year-long effort to jump start this next generation of smart and presumably helpful robots by offering a cash prize for the designers of a machine that could work in concert with human controllers in a hazardous environment. The $3.5 million competition was won by a South Korean team from the Korean Advanced Institute of Science and Technology. The technology may still seem far-fetched, but betting against the agency that has had a remarkably far-reaching effect on the modern world -- from funding the work that led to both the personal computer and the Internet, to setting expectations that self-driving vehicles are only a matter of years away -- might be a mistake. Darpa officials have taken pains to assure anyone who would listen that it was not primarily interested in designing Terminators, or killer robots. The agency is an arm of the Pentagon, and its futuristic robots are an example of what is described as a "dual use" technology that will have both military and civilian uses. Darpa, which is also known for pioneering the Internet surveillance system that was exposed last year by Edward J. Snowden, has, under its current director, Arati Prabhakar, expanded its watchfulness over the potential effect of the technologies it helps foster. In introducing a workshop for discussion on the effect of robotics held at the end of the challenge competition on Sunday, Dr. Prabhakar described the agency as being committed to a broader mission: "We work together to build the future of robots that can help extend the capabilities that we have and build the technologies that will aid humanity in the future.

Apple unveiled a smarter Siri personal assistant on Monday that's picked up some features already offered by Google, but emphasized that it's improving Siri without compromising the company's commitment to user privacy. An update to the iPhone and iPad operating system coming this fall will deliver a Siri that's able to search through more apps than ever and offer users' information based on what it thinks they might want to know. That includes automatically adding event invitations to the Calendar app, telling iPhone holders who might be calling based on an unknown number matching one in an email and launching the Music app when someone plugs in headphones in the morning because that's become their routine. Apple made the announcement to kick off its weeklong Worldwide Developers Conference, a gathering for appmakers to learn about Apple products. "We think these kind of intelligence features make a huge difference in iOS 9," Craig Federighi, Apple's senior vice president of software engineering, told an audience of media and software developers at the Moscone Center in San Francisco.

A linear function submission-based double-auction (LFS-DA) mechanism for a regional electricity network is proposed in this paper. Each agent in the network is equipped with a battery and a generator. Each agent simultaneously becomes a producer and consumer of electricity, i.e., a prosumer and trades electricity in the regional market at a variable price. In the LFS-DA, each agent uses linear demand and supply functions when they submit bids and asks to an auctioneer in the regional market.The LFS-DA can achieve an exact balance between electricity demand and supply for each time slot throughout the learning phase and was shown capable of solving the primal problem of maximizing the social welfare of the network without any central price setter, e.g., a utility or a large electricity company, in contrast with conventional real-time pricing (RTP). This paper presents a clarification of the relationship between the RTP algorithm derived on the basis of a dual decomposition framework and LFS-DA. Specifically, we proved that the changes in the price profile of the LFS-DA mechanism are equal to those achieved by the RTP mechanism derived from the dual decomposition framework except for a constant factor.

Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models. In this paper, we study how pseudolikelihood can be derived for learning parameters of a mixture of Ising models. The performance of the proposed approach is demonstrated for Ising and Potts models on both synthetic and real data.