New publication: “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey”

Home » Scientific Publications » New publication: “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey”

We are thrilled to announce that we have published a new paper entitled “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey” on the Springer Artificial Intelligence Review Journal.

The paper, that is published as Open Access and can be downloaded following its doi: 10.1007/s10462-018-09679-z, is authored by Giang Nguyen, Stefan Dlugolinsky, Martin Bobák, Viet Tran, Álvaro López García, Ignacio Heredia, Peter Malík and Ladislav Hluchý, from the Institute of Informatics Slovak Academy of Sciences (IISAS) and the Institute of Physics of Cantabria (IFCA – CSIC – UC).

Abstract: The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

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