BigDataFr recommends: Bayesian Nonlinear Support Vector Machines for Big Data […] We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional […]
Documentation
[Datasciencecentral] BigDataFr recommends: Better Banking with help of Analytics and Machine learning
BigDataFr recommends: Better Banking with help of Analytics and Machine learning […] In 2015, I was working at Diebold where we build ATM machine hardware and software and complete ecosystem around the ATM. When we talk about ATM machine, it is a collection of very complex small hardware which collectively performs tasks. And typically, when […]
[Datasciencecentral] BigDataFr recommends: Big data in Retail industry
BigDataFr recommends: Big data in Retail industry […] 1. Objective Through this blog you will know about the Big data applications and Big data uses in retail sector with examples along with use of RFID in retail. You will learn concepts related to Big data in retail industry like retail asset management, production tracking in […]
[arXiv] BigDataFr recommends: Big Data vs. complex physical models: a scalable inference algorithm
BigDataFr recommends: Big Data vs. complex physical models: a scalable inference algorithm […] The data torrent unleashed by current and upcoming instruments requires scalable analysis methods. Machine Learning approaches scale well. However, separating the instrument measurement from the physical effects of interest, dealing with variable errors, and deriving parameter uncertainties is usually an after-thought. Classic […]
[Datasciencecentral] BigDataFr recommends: Embracing Conflict to Fuel Digital Innovation
BigDataFr recommends: Embracing Conflict to Fuel Digital Innovation […] When talking to clients about their business goals, most business executives are pretty clear as to what they want to accomplish, such as reducing customer churn or reducing inventory costs or improving quality of care or improving product line profitability. But these “one dimensional” business initiatives […]
[arXiv] BigDataFr recommends: A K-means clustering algorithm for multivariate big data with correlated components
BigDataFr recommends: A K-means clustering algorithm for multivariate big data with correlated components […] Common clustering algorithms require multiple scans of all the data to achieve convergence, and this is prohibitive when large databases, with millions of data, must be processed. Some algorithms to extend the popular K-means method to the analysis of big data […]
[Datasciencecentral] BigDataFr recommends: What tomorrow’s business leaders need to know about Machine Learning?
BigDataFr recommends: What tomorrow’s business leaders need to know about Machine Learning? […] Sometimes I write a blog just to formulate and organize a point of view, and I think it’s time that I pull together the bounty of excellent information about Machine Learning. This is a topic with which business leaders must become comfortable, […]
[arXiv] BigDataFr recommends: From Big Data to Big Displays: High-Performance Visualization at Blue Brain
BigDataFr recommends: From Big Data to Big Displays: High-Performance Visualization at Blue Brain […] Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present […]
[Google Research Blog] BigDataFr recommends: MultiModel: Multi-Task Machine Learning Across Domains
BigDataFr recommends: MultiModel: Multi-Task Machine Learning Across Domains […] Over the last decade, the application and performance of Deep Learning has progressed at an astonishing rate. However, the current state of the field is that the neural network architectures are highly specialized to specific domains of application. An important question remains unanswered: Will a convergence […]
[arXiv] BigDataFr recommends: Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models
BigDataFr recommends: Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models […] With the tremendous increase in the amount of biological literature, developing automated methods for extracting big data from papers, building models and explaining big mechanisms becomes a necessity. We describe here our approach to translating machine reading outputs, obtained by […]