Zobrazeno 1 - 10
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pro vyhledávání: '"A P Dolgov"'
We develop both first and second order numerical optimization methods to solve non-smooth optimization problems featuring a shared sparsity penalty, constrained by differential equations with uncertainty. To alleviate the curse of dimensionality we u
Externí odkaz:
http://arxiv.org/abs/2411.03989
The origin of the ultra high energy cosmic rays via annihilation of heavy stable, fermions "f", of the cosmological dark matter (DM) is studied. The particles in question are supposed to be created by the scalaron decays in $R^2$ modified gravity. No
Externí odkaz:
http://arxiv.org/abs/2405.12560
Autor:
Termanova, A., Melnikov, Ar., Mamenchikov, E., Belokonev, N., Dolgov, S., Berezutskii, A., Ellerbrock, R., Mansell, C., Perelshtein, M.
Running quantum algorithms often involves implementing complex quantum circuits with such a large number of multi-qubit gates that the challenge of tackling practical applications appears daunting. To date, no experiments have successfully demonstrat
Externí odkaz:
http://arxiv.org/abs/2403.13486
Autor:
Abronin, V., Naumov, A., Mazur, D., Bystrov, D., Tsarova, K., Melnikov, Ar., Oseledets, I., Dolgov, S., Brasher, R., Perelshtein, M.
We introduce TQCompressor, a novel method for neural network model compression with improved tensor decompositions. We explore the challenges posed by the computational and storage demands of pre-trained language models in NLP tasks and propose a per
Externí odkaz:
http://arxiv.org/abs/2401.16367
Autor:
Janjoš, Faris, Hallgarten, Marcel, Knittel, Anthony, Dolgov, Maxim, Zell, Andreas, Zöllner, J. Marius
The CVAE is one of the most widely-used models in trajectory prediction for AD. It captures the interplay between a driving context and its ground-truth future into a probabilistic latent space and uses it to produce predictions. In this paper, we ch
Externí odkaz:
http://arxiv.org/abs/2310.19944
Autor:
Sergey Dolgov, Dmitry Savostyanov
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-26 (2024)
Abstract We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possib
Externí odkaz:
https://doaj.org/article/9323d51c8f8f4bdd85d9a7d3614c2643
The Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables. Interpreting its reconstruction process as a nonlinear transformation of samples from the latent posterior distribution, we apply the Unscented
Externí odkaz:
http://arxiv.org/abs/2306.05256
Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Although one-shot approaches are usua
Externí odkaz:
http://arxiv.org/abs/2306.03367
We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task necessarily requires huge amounts of real-world data or highly realistic simulation. Focusing on a roundabout scenario, we show th
Externí odkaz:
http://arxiv.org/abs/2305.18942
Autor:
Kornev, Egor, Dolgov, Sergey, Pinto, Karan, Pflitsch, Markus, Perelshtein, Michael, Melnikov, Artem
The solution of computational fluid dynamics problems is one of the most computationally hard tasks, especially in the case of complex geometries and turbulent flow regimes. We propose to use Tensor Train (TT) methods, which possess logarithmic compl
Externí odkaz:
http://arxiv.org/abs/2305.10784