Time-varying mixture densities occur in many scenarios, for example, the distributions of keywords that appear in publications may evolve from year to year, video frame features associated with multiple targets may evolve in a sequence. Any models that realistically cater to this phenomenon must exhibit two important properties: the underlying mixture densities must have an unknown number of mixtures, and there must be some "smoothness" constraints in place for the adjacent mixture densities. The traditional Hierarchical Dirichlet Process (HDP) may be suited to the first property, but certainly not the second. This is due to how each random measure in the lower hierarchies is sampled independent of each other and hence does not facilitate any temporal correlations. To overcome such shortcomings, we proposed a new Smoothed Hierarchical Dirichlet Process (sHDP). The key novelty of this model is that we place a temporal constraint amongst the nearby discrete measures $\{G_j\}$ in the form of symmetric Kullback-Leibler (KL) Divergence with a fixed bound $B$. Although the constraint we place only involves a single scalar value, it nonetheless allows for flexibility in the corresponding successive measures. Remarkably, it also led us to infer the model within the stick-breaking process where the traditional Beta distribution used in stick-breaking is now replaced by a new constraint calculated from $B$. We present the inference algorithm and elaborate on its solutions. Our experiment using NIPS keywords has shown the desirable effect of the model.

I am attending the Nexus of Information and Computation Theories workshop at the Institut Henri Poincaré in Paris this 7 days. It is the very last 7 days of a ten 7 days system that brought collectively researchers from information and facts principle and CS principle in workshops all over various themes these kinds of as distributed computation, inference, lower bounds, inequalities, and security/privateness. The very last two months are on Privateness and Protection: I assisted manage these two months with Prakash Narayan, Salil Vadhan, Aaron Roth, and Vinod Vaikuntanathan. Due to training and ICASSP, I skipped very last 7 days, but am here for this 7 days, for which the sub-subjects are security multiparty computation and differential privateness. The structure of the workshop was to have four tutorials (two for each 7 days) and then a established of with any luck, similar talks.

Something is wrong in the way ML is being taught to developers. Most ML teachers like to explain how different learning algorithms work and spend tons of time on that. For a beginner who wants to start using ML, being able to choose an algorithm and set parameters looks like the #1 barrier to entry, and knowing how the different techniques work seems to be a key requirement to remove that barrier. Many practitioners argue however that you only need one technique to get started: random forests. Other techniques may sometimes outperform them, but in general, random forests are the most likely to perform best on a variety of problems (see Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?), which makes them more than enough for a developer just getting started with ML.

In machine learning, the problem of classification entails correctly identifying to which class or group a new observation belongs, by learning from observations whose classes are already known. In what follows, I will build a classification experiment in Azure ML Studio to predict wine quality based on physicochemical data. Several classification algorithms will be applied on the data set and the performance of these algorithms will be compared. I will also present a tutorial on how to do similar exercise using MRS (Microsoft R Server, formerly Revolution R Enterprise). I will use wine quality data set from the UCI Machine Learning Repository.

When we go grocery shopping, we often have a standard list of things to buy. Each shopper has a distinctive list, depending on one's needs and preferences. A housewife might buy healthy ingredients for a family dinner, while a bachelor might buy beer and chips. Understanding these buying patterns can help to increase sales in several ways. While we may know that certain items are frequently bought together, the question is, how do we uncover these associations? Besides increasing sales profits, association rules can also be used in other fields.

There are so many deep learning libraries to choose from. Which are the good professional libraries that are worth learning and which are someones side project and should be avoided. It is hard to tell the difference. In this post you will discover the top deep learning libraries that you should consider learning and using in your own deep learning project. Popular Deep Learning Libraries Photo by Nikki, some rights reserved.

Optimization provides a valuable framework for thinking about, formulating, and solving many problems in machine learning. Since specialized techniques for the quadratic programming problem arising in support vector classification were developed in the 1990s, there has been more and more cross-fertilization between optimization and machine learning, with the large size and computational demands of machine learning applications driving much recent algorithmic research in optimization. This tutorial reviews the major computational paradigms in machine learning that are amenable to optimization algorithms, then discusses the algorithmic tools that are being brought to bear on such applications. We focus particularly on such algorithmic tools of recent interest as stochastic and incremental gradient methods, online optimization, augmented Lagrangian methods, and the various tools that have been applied recently in sparse and regularized optimization.

In the late 1960s, urban planners Horst Rittel and Melvin Webber began formulating the concept of "wicked problems" or "wicked challenges" --problems so vexing in the realm of social and organizational planning that they could not be successfully ameliorated with traditional linear, analytical, systems-engineering types of approaches. These "wicked challenges" are poorly defined, abstruse, and connected to strong moral, political and professional issues. Some examples might include: "How should we deal with crime and violence in our schools? "How should we wage the'War on Terror'? or "What is good national immigration policy?" "Wicked problems," by their very nature, are strongly stakeholder dependent; there is often little consensus even about what the problem is, let alone how to deal with it.

While video becomes more important as a digital media type, video data often remains dark to analytics. This webinar demonstrates how IBM Bluemix OpenWhisk together with Watson services begins to unlock the value of video data. Dark Vision is an application that uploads video files or streams to the cloud, transcodes video data, extracts and passes frames through the Watson Image Recognition and the Alchemy Face Recognition services, and generates meta-data to use in categorizing the video data for searchability. In the presentation, Andreas Nauerz introduces the basics of IBM Bluemix OpenWhisk and Frederic Lavigne demonstrates how to create OpenWhisk code to accomplish Dark Vision's application workflow. Maybe you want to do that because a particular team already has developed skills in this area, maybe because they do mobile development or have done mobile development in the past, and now want to outsource computer-intensive tasks.

The assumption that a college education should take four years is baked into American culture. Colleges in the colonial days were founded on the premise of a four-year degree, a concept imported from Europe. Harvard University experimented with a three-year degree when it was founded in 1636, but the test was short-lived, and the four-year degree has been the standard ever since. We expect students to enter college at 18 and leave when they turn 22, and we worry about those who take a more circuitous route to graduation. But we need to reconsider that long-established, one-size-fits all model.