Search Results for author: Vladimir Ivashkin

Found 7 papers, 2 papers with code

Dissecting graph measure performance for node clustering in LFR parameter space

1 code implementation20 Feb 2022 Vladimir Ivashkin, Pavel Chebotarev

While usually measure comparisons are limited to general measure ranking on a particular dataset, we aim to explore the performance of various measures depending on graph features.

Node Clustering

Dissecting graph measures performance for node clustering in LFR parameter space

no code implementations1 Jan 2021 Vladimir Ivashkin, Pavel Chebotarev

Given particular graph parameters, this allows us to choose the best measure to use for clustering.

Node Clustering

Image-to-image Neural Network for Addition and Subtraction of a Pair of Not Very Large Numbers

no code implementations14 Mar 2020 Vladimir Ivashkin

Looking back at the history of calculators, one can see that they become less functional and more computationally expensive over time.

Precipitation Nowcasting with Satellite Imagery

no code implementations23 May 2019 Vadim Lebedev, Vladimir Ivashkin, Irina Rudenko, Alexander Ganshin, Alexander Molchanov, Sergey Ovcharenko, Ruslan Grokhovetskiy, Ivan Bushmarinov, Dmitry Solomentsev

Precipitation nowcasting is a short-range forecast of rain/snow (up to 2 hours), often displayed on top of the geographical map by the weather service.

Optical Flow Estimation

Spatiotemporal Data Fusion for Precipitation Nowcasting

no code implementations28 Dec 2018 Vladimir Ivashkin, Vadim Lebedev

Precipitation nowcasting using neural networks and ground-based radars has become one of the key components of modern weather prediction services, but it is limited to the regions covered by ground-based radars.

Do logarithmic proximity measures outperform plain ones in graph clustering?

no code implementations3 May 2016 Vladimir Ivashkin, Pavel Chebotarev

A possible origin of this effect is that most kernels have a multiplicative nature, while the nature of distances used in cluster algorithms is an additive one (cf.

Graph Clustering

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