User profiles for Yermek Kapushev
 | Yermek KapushevAIRI Verified email at airi.net Cited by 272 |
Quadrature-based features for kernel approximation
M Munkhoeva, Y Kapushev… - Advances in neural …, 2018 - proceedings.neurips.cc
We consider the problem of improving kernel approximation via randomized feature maps.
These maps arise as Monte Carlo approximation to integral representations of kernel …
These maps arise as Monte Carlo approximation to integral representations of kernel …
Gaussian process regression for structured data sets
Approximation algorithms are widely used in many engineering problems. To obtain a data
set for approximation a factorial design of experiments is often used. In such case the size of …
set for approximation a factorial design of experiments is often used. In such case the size of …
Large-scale shape retrieval with sparse 3d convolutional neural networks
In this paper we present results of performance evaluation of S3DCNN — a Sparse 3D
Convolutional Neural Network — on a large-scale 3D Shape benchmark ModelNet40, and …
Convolutional Neural Network — on a large-scale 3D Shape benchmark ModelNet40, and …
Exact inference for gaussian process regression in case of big data with the cartesian product structure
M Belyaev, E Burnaev, Y Kapushev - arXiv preprint arXiv:1403.6573, 2014 - arxiv.org
Approximation algorithms are widely used in many engineering problems. To obtain a data
set for approximation a factorial design of experiments is often used. In such case the size of …
set for approximation a factorial design of experiments is often used. In such case the size of …
Beyond Attention: Breaking the Limits of Transformer Context Length with Recurrent Memory
A major limitation for the broader scope of problems solvable by transformers is the
quadratic scaling of computational complexity with input size. In this study, we investigate the …
quadratic scaling of computational complexity with input size. In this study, we investigate the …
Denoising Score Matching via Random Fourier Features
The density estimation is one of the core problems in statistics. Despite this, existing techniques
like maximum likelihood estimation are computationally inefficient in case of complex …
like maximum likelihood estimation are computationally inefficient in case of complex …
Tensor Completion via Gaussian Process--Based Initialization
In this paper, we consider the tensor completion problem representing the solution in the
tensor train (TT) format. It is assumed that the tensor is of high order, and tensor values are …
tensor train (TT) format. It is assumed that the tensor is of high order, and tensor values are …
Random fourier features based slam
Y Kapushev, A Kishkun, G Ferrer… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
This work is dedicated to simultaneous continuous-time trajectory estimation and mapping
based on Gaussian Processes (GP). State-of-the-art GP-based models for Simultaneous …
based on Gaussian Processes (GP). State-of-the-art GP-based models for Simultaneous …
Accurate fetal variant calling in the presence of maternal cell contamination
E Nabieva, SM Sharma, Y Kapushev… - European Journal of …, 2020 - nature.com
High-throughput sequencing of fetal DNA is a promising and increasingly common method
for the discovery of all (or all coding) genetic variants in the fetus, either as part of prenatal …
for the discovery of all (or all coding) genetic variants in the fetus, either as part of prenatal …
[PDF][PDF] Exact inference for Gaussian process regression in case of big data with the Cartesian product structure
B Mikhail, B Evgeny, K Yermek - ICML workshop on New …, 2014 - pdfs.semanticscholar.org
… Belyaev Mikhail1,2,3, Burnaev Evgeny1,2,3, Kapushev Yermek1,2 … More details are
given in Belyaev, M., Burnaev, E., and Kapushev, Y. (2014). Exact inference for gaussian …
given in Belyaev, M., Burnaev, E., and Kapushev, Y. (2014). Exact inference for gaussian …