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

28 citations · 53 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC20171 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.DC201720 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…

cs.HC20174 cited

Automatized Generation of Alphabets of Symbols

Serhii Hamotskyi, Anis Rojbi, Sergii Stirenko +1

In this paper, we discuss the generation of symbols (and alphabets) based on specific user requirements (medium, priorities, type of information that needs to be conveyed). A frame…

cs.LG201728 cited

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

Yuriy Kochura, Sergii Stirenko, Anis Rojbi +3

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…