BigDataFr recommends: Deep Broad Learning – Big Models for Big Data ‘Deep learning has demonstrated the power of detailed modeling of complex high-order (multivariate) interactions in data. For some learning tasks there is power in learning models that are not only Deep but also Broad. […] The most accurate models will integrate all that information. […]
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[arXiv] BigDataFr recommends: Improving Big Data Visual Analytics with Interactive Virtual Reality #datascientist #machine learning
BigDataFr recommends: Improving Big Data Visual Analytics with Interactive Virtual Reality ‘For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined ‘Big Data’, massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, […]
[arXiv] BigDataFr recommends:A Flexible Coordinate Descent Method for Big Data Applications
BigDataFr recommends: A Flexible Coordinate Descent Method for Big Data Applications ‘In this paper we present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate) partial second-order (curvature) information, so that the algorithm performance is more robust when applied to highly nonseparable or ill […]
[arXiv] BigDataFr recommends: Predicting Regional Economic Indices using Big Data of Individual Bank Card Transactions #machine learning #datascientist
BigDataFr recommends: Predicting Regional Economic Indices using Big Data of Individual Bank Card Transactions ‘For centuries quality of life was a subject of studies across different disciplines. However, only with the emergence of a digital era, it became possible to investigate this topic on a larger scale. Over time it became clear that quality of […]
[arXiv] BigDataFr recommends: Behaviour of ABC for Big Data #datascientist #machinelearning
BigDataFr recommends: Behaviour of ABC for Big Data ‘Many statistical applications involve models that it is difficult to evaluate the likelihood, but relatively easy to sample from, which is called intractable likelihood. Approximate Bayesian computation (ABC) is a useful Monte Carlo method for inference of the unknown parameter in the intractable likelihood problem under Bayesian […]
[arXiv] BigDataFr recommends: Benchmarking Big Data Systems – State-of-the-Art and Future Directions #datascientist #machinelearning
BigDataFr recommends: Benchmarking Big Data Systems – State-of-the-Art and Future Directions ‘The great prosperity of big data systems such as Hadoop in recent years makes the benchmarking of these systems become crucial for both research and industry communities. The complexity, diversity, and rapid evolution of big data systems gives rise to various new challenges about […]
[arXiv] BigDataFr recommends: Identifying Dwarfs Workloads in Big Data Analytics #datascientist #machinelearning
BigDataFr recommends: Identifying Dwarfs Workloads in Big Data Analytics ‘Big data benchmarking is particularly important and provides applicable yardsticks for evaluating booming big data systems. However, wide coverage and great complexity of big data computing impose big challenges on big data benchmarking. How can we construct a benchmark suite using a minimum set of units […]
[arXiv] BigDataFr recommends: Online Updating of Statistical Inference in the Big Data Setting
BigDataFr recommends: Online Updating of Statistical Inference in the Big Data Setting ‘We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models […]
[arxiv] BigDataFr recommends: Computing on Masked Data to improve the Security of Big Data
BigDataFr recommends : Computing on Masked Data to improve the Security of Big Data Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need […]
[arXiv] BigDataFr recommends: Network Filtering for Big Data
BigDataFr recommends: Network Filtering for Big Data: Triangulated Maximally Filtered Graph ‘We propose a network-filtering method, the Triangulated Maximally Filtered Graph (TMFG), that provides an approximate solution to the Weighted Maximal Planar Graph problem. The underlying idea of TMFG consists in building a triangulation that maximizes a score function associated with the amount of information […]