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20172022
most citedComparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes

28 citations · 138 across the 12 of their papers we have counts for

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8 papers · 1 filter

cs.CY2017★ 15 cited

Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions

Yuri Gordienko, Sergii Stirenko, Yuriy Kochura +3

The new method is proposed to monitor the level of current physical load and accumulated fatigue by several objective and subjective characteristics. It was applied to the dataset…

cs.HC2017★ 1 cited

Generating and Estimating Nonverbal Alphabets for Situated and Multimodal Communications

Serhii Hamotskyi, Sergii Stirenko, Yuri Gordienko +1

In this paper, we discuss the formalized approach for generating and estimating symbols (and alphabets), which can be communicated by the wide range of non-verbal means based on sp…

cs.SD2017

Music Transcription by Deep Learning with Data and "Artificial Semantic" Augmentation

Vladyslav Sarnatskyi, Vadym Ovcharenko, Mariia Tkachenko +3

In this progress paper the previous results of the single note recognition by deep learning are presented. The several ways for data augmentation and "artificial semantic" augmenta…

cs.LG2017★ 7 cited

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

Yuriy Kochura, Sergii Stirenko, Oleg Alienin +2

The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their c…

cs.LG2017★ 18 cited

Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions

Yuriy Kochura, Sergii Stirenko, Yuri Gordienko

Deep learning (deep structured learning, hierarchi- cal learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-…

cs.DC2017★ 20 cited

Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks

Vladyslav Taran, Oleg Alienin, Sergii Stirenko +2

Recently, due to rapid development of information and communication technologies, the data are created and consumed in the avalanche way. Distributed computing create preconditions…