460 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National Academy of Sciences of UkraineUA46 papers
- Bogolyubov Institute for Theoretical PhysicsUA36 papers
- Institute of PhysicsPL31 papers
- Taras Shevchenko National University of KyivUA21 papers
- Institute of MagnetismUA16 papers
- Institute for Information RecordingUA13 papers
- Clarkson UniversityUS12 papers
- FZU ‒ Institute of Physics of the Academy of Sciences of the Czech RepublicCZ12 papers
- Institute of MathematicsPL12 papers
- Frantsevich Institute for Problems in Materials ScienceUA11 papers
- Institute for Nuclear ResearchRU9 papers
- Johannes Gutenberg University MainzDE8 papers
11 papers · 1 filter
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Robust Clustering on High-Dimensional Data with Stochastic Quantization
Anton Kozyriev, Vladimir Norkin
This paper addresses the limitations of conventional vector quantization algorithms, particularly K-Means and its variant K-Means++, and investigates the Stochastic Quantization (S…
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…
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti
Nikita Gordienko, Peng Gang, Yuri Gordienko +4
Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukrain…
Parallel Statistical and Machine Learning Methods for Estimation of Physical Load
Sergii Stirenko, Gang Peng, Wei Zeng +4
Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue . They are based on the statistical analys…
Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation
Sergii Stirenko, Yuriy Kochura, Oleg Alienin +4
The results of chest X-ray (CXR) analysis of 2D images to get the statistically reliable predictions (availability of tuberculosis) by computer-aided diagnosis (CADx) on the basis…
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer
Yu. Gordienko, Yu. Kochura, O. Alienin +5
Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outl…
Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer
Yu. Gordienko, Peng Gang, Jiang Hui +5
The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from…
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…
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-…
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…