Search Results for author: Yuri Gordienko

Found 14 papers, 3 papers with code

Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercises

no code implementations20 Dec 2019 Peng Gang, Wei Zeng, Yuri Gordienko, Oleksandr Rokovyi, Oleg Alienin, Sergii Stirenko

The classification problem was to predict the known level of the in-exercise loads (in three categories by calories) by the heart rate activity features measured during the short period of time (1 minute only) after training, i. e by features of the post-exercise load.

Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases

no code implementations31 Dec 2018 Yuriy Kochura, Yuri Gordienko, Vlad Taran, Nikita Gordienko, Alexandr Rokovyi, Oleg Alienin, Sergii Stirenko

The significant speedup was obtained even for extremely low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training stage (without taking into account the overheads) and speedup up to 2x for prediction stage (with and without taking into account overheads).

Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions

1 code implementation30 Dec 2018 Vlad Taran, Yuri Gordienko, Alexandr Rokovyi, Oleg Alienin, Sergii Stirenko

The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa.

Autonomous Driving Scene Understanding +1

Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti

no code implementations31 Aug 2018 Nikita Gordienko, Peng Gang, Yuri Gordienko, Wei Zeng, Oleg Alienin, Oleksandr Rokovyi, Sergii Stirenko

A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods.

Data Augmentation Dimensionality Reduction

Parallel Statistical and Machine Learning Methods for Estimation of Physical Load

no code implementations14 Aug 2018 Sergii Stirenko, Gang Peng, Wei Zeng, Yuri Gordienko, Oleg Alienin, Oleksandr Rokovyi, Nikita Gordienko

Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue .

Performance Evaluation of Deep Learning Networks for Semantic Segmentation of Traffic Stereo-Pair Images

no code implementations5 Jun 2018 Vlad Taran, Nikita Gordienko, Yuriy Kochura, Yuri Gordienko, Alexandr Rokovyi, Oleg Alienin, Sergii Stirenko

Here the results of application of several deep learning architectures (PSPNet and ICNet) for semantic image segmentation of traffic stereo-pair images are presented.

Self-Driving Cars Semantic Segmentation

Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation

1 code implementation3 Mar 2018 Sergii Stirenko, Yuriy Kochura, Oleg Alienin, Oleksandr Rokovyi, Peng Gang, Wei Zeng, Yuri Gordienko

Lossless data augmentation of the segmented dataset leads to the lowest validation loss (without overfitting) and nearly the same accuracy (within the limits of standard deviation) in comparison to the original and other pre-processed datasets after lossy data augmentation.

Data Augmentation

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

no code implementations30 Dec 2017 Yuri Gordienko, Sergii Stirenko, Yuriy Kochura, Oleg Alienin, Michail Novotarskiy, Nikita Gordienko

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

Generating and Estimating Nonverbal Alphabets for Situated and Multimodal Communications

no code implementations12 Dec 2017 Serhii Hamotskyi, Sergii Stirenko, Yuri Gordienko, Anis Rojbi

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 specific user requirements (medium, priorities, type of information that needs to be conveyed).

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

1 code implementation29 Aug 2017 Yuriy Kochura, Sergii Stirenko, Oleg Alienin, Michail Novotarskiy, Yuri Gordienko

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.

Hyperparameter Optimization

Automatized Generation of Alphabets of Symbols

no code implementations16 Jul 2017 Serhii Hamotskyi, Anis Rojbi, Sergii Stirenko, Yuri Gordienko

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).

Eye Tracking

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

no code implementations16 Jul 2017 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- level abstractions in data by using multiple processing layers with complex structures or otherwise composed of multiple non-linear transformations.

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

no code implementations7 Jun 2017 Yuriy Kochura, Sergii Stirenko, Anis Rojbi, Oleg Alienin, Michail Novotarskiy, Yuri Gordienko

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.