ANN-Benchmarks This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard interface for measuring the performance and quality achieved by nearest neighbor algorithms on different standard data sets. It supports several different ways of integrating $k$-NN algorithms, and its configuration system automatically tests a range of parameter settings for each algorithm. Algorithms are compared with respect to many different (approximate) quality measures, and adding more is easy and fast; the included plotting front-ends can visualise these as images, $\LaTeX$ plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of $k$-NN algorithms. In the short term, this overview allows users to choose the correct $k$-NN algorithm and parameters for their similarity search task; in the longer term, algorithm designers will be able to use this overview to test and refine automatic parameter tuning. The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work. Interestingly, very different approaches to $k$-NN search yield comparable quality-performance trade-offs. The system is available at http://ann-benchmarks.com. …

Two-Year Post Doctoral Fellowship in Forest Ecological Forecasting, Data Assimilation

Cause of death has changed over the years. In 1999, the suicide rate among 25- to 34-year-olds was 12.7 per 100,000 people. By 2016, that rate was almost 30 percent higher at 16.5.

Spatial Variable Selection (SpatialVS)Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the i …

Dataset: Rare Event Classification in Multivariate Time Series

Microsoft Ignite, Microsoft’s annual developer conference, wrapped up last week and many of the big announcements focused on artificial intelligence and machine learning. The keynote presentation from Microsoft’s Cloud AI lead Eric Boyd showcases the major developments, or you can check out his accompanying blog post for a written summary. 

If you use Python* for HPC, data analytics, or data science, you love the productivity features and the large ecosystem of packages available to quickly build applications. However, getting faster performance from Python that’s close to native code is difficult, requiring software development expertise and tremendous patience.

In 2018, it seemed like a day barely goes by without someone announcing the opening of a new AI lab (MIT, IBM, Alibaba, Baidu, Intel, Google, Facebook - the list goes on). And yes, this is another one of those announcements… sort of. But here’s why the launch of the Enterprise AI Lab is different.

Commenter Chris G pointed out this quote from Ta-Nehisi Coates:

“The Last Mile of Analytics1. Succeeding in Analytics and getting to the Last Mile of embedding Analytics in decision-making is not just about dealing with data. The journey from Data to Decisions requires one to look at how Analytics is operationalized in the business. And the missing piece that actually can drive or enable Analytics is not data but Culture. Can culture be enabled by Analytics?2. To make Analytics actually a part of the Company culture, it’s not enough to have a set of people providing Analytics solutions. Analytics needs to be embedded in technology accelerators that can directly enable decisions at the point of action. This makes Analytics accountable for real business decisions rather than just providing more data.3. Making Analytics work across the business requires collaboration and the right choice of Engagement Model which would vary based on the maturity of the organization and its decision-making, not its data needs. The business models could range from Products, Services, and Managed Solutions to Analytic Marketplaces. Unless the right business model is chosen, Analytics will remain a discrete project.” Debleena Roy ( 30. July 2015 )