Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

By Giang Nguyen, Stefan Dlugolinsky, Martin Bobák, Viet D. Tran, Alvaro Lopez Garcia, Ignacio Heredia, Peter Malík, Ladislav Hluchý. Artificial Intelligence Review, 2019

759 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/23bae60509630acda34cb1811125de3a0cb86cd7

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Citations in the corpus

Deep learning tools for the measurement of animal behavior in neuroscience (2019, methodology): “Many of the properties of DNNs were extremely appealing: remarkable and robust performance, relatively fast inference due to GPU hardware, and efﬁcient code due to modern packages like TensorFlow and PyTorch (reviewed in (47)).”
Surgical data science – from concepts toward clinical translation (2020, methodology): “NiftyNet (specifically for medical image data), and PyTorch’s TorchVision (Nguyen et al., 2019).”
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends (2020, methodology): “One strength of Theano was the calculation of gradients by automatic differentiation of designed networks as well as CPU and GPU processing inspiring today’s leading DL frameworks [139,140].”
A survey of the recent architectures of deep convolutional neural networks (2019, methodology): “ted using graphics Accepted in Artificial Intelligence Review (Springer Nature), DOI: 10.1007/s10462-020-09825-6 15 processing units (GPUs) to accelerate the training of deep NN and CNN architectures [90]–[93]. In 2007, NVIDIA launched the CUDA programming platform, which allows exploitation of para”
Learning From Noisy Labels With Deep Neural Networks: A Survey (2020, background): “On the other hand, the need for more efﬁcient algorithms is increasing owing to the rapid increase in the amount of available data [174].”
Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things (2020, background): “50,51,301 A self-powered neural finger skin is developed with a triboelectric layer and a graphene sensor array, mimicking the fast, and the slow adaptive mechanoreceptors in human skin, to continuously monitor the applied pressure (Figure 10D).”