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

23 citations · 62 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG201823 cited

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

Yuriy Kochura, Yuri Gordienko, Vlad Taran +4

The impact of the maximally possible batch size (for the better runtime) on performance of graphic processing units (GPU) and tensor processing units (TPU) during training and infe…

cs.CV201819 cited

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

Vlad Taran, Yuri Gordienko, Alexandr Rokovyi +2

Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The app…

cs.CV2018

Capsule Deep Neural Network for Recognition of Historical Graffiti Handwriting

Nikita Gordienko, Yuriy Kochura, Vlad Taran +3

Automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine) was demonstrated by means of capsule deep l…

cs.CV2018

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

Vlad Taran, Nikita Gordienko, Yuriy Kochura +4

Semantic image segmentation is one the most demanding task, especially for analysis of traffic conditions for self-driving cars. Here the results of application of several deep lea…

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…