Denis Derkach

According to our database, Denis Derkach authored at least 25 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors.

Stepan Svirin, Artem Ryzhikov, Saraa Ali, Denis Derkach

CoRR, 2024

Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space.

Tigran Ramazyan, Mikhail Hushchyn, Denis Derkach

Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023

Symbolic expression generation <i>via</i> variational auto-encoder.

Sergei Popov, Mikhail Lazarev, Vladislav Belavin, Denis Derkach, Andrey Ustyuzhanin

PeerJ Comput. Sci., 2023

The LHCb ultra-fast simulation option, Lamarr: design and validation.

Lucio Anderlini, Matteo Barbetti, Simone Capelli, Gloria Corti, Adam Davis, Denis Derkach, Nikita Kazeev, Artem Maevskiy, Maurizio Martinelli, Sergei Mokonenko, Benedetto Gianluca Siddi, Zehua Xu

CoRR, 2023

Symbolic expression generation via Variational Auto-Encoder.

Sergei Popov, Mikhail Lazarev, Vladislav Belavin, Denis Derkach, Andrey Ustyuzhanin

CoRR, 2023

Latent Stochastic Differential Equations for Change Point Detection.

Artem Ryzhikov, Mikhail Hushchyn, Denis Derkach

IEEE Access, 2023

2022

A Comparison of CPU and GPU Implementations for the LHCb Experiment Run 3 Trigger.

Roel Aaij, Marta Adinolfi, Salvatore Aiola, S. Akar, Johannes Albrecht, M. Alexander, S. Amato, Yasmine Amhis, F. Archilli, M. Bala, G. Bassi, L. Bian, M. P. Blago, Thomas Boettcher, A. Boldyrev, S. Borghi, A. Brea Rodriguez, Lukas Calefice, M. Calvo Gomez, Daniel Hugo Cámpora Pérez, A. Cardini, Marco Cattaneo, V. Chobanova, G. Ciezarek, X. Cid Vidal, J. L. Cobbledick, J. A. B. Coelho, Tommaso Colombo, A. Contu, Benjamin Couturier, Daniel C. Craik, R. Currie, P. d'Argent, Michel De Cian, Denis Derkach, F. Dordei, Marco Dorigo, L. Dufour, Paolo Durante, A. Dziurda, A. Dzyuba, S. Easo, S. Esen, Placido Fernandez Declara, S. Filippov, Conor Fitzpatrick, M. Frank, Paolo Gandini, Vladimir V. Gligorov, Elisabet Golobardes, G. Graziani, L. Grillo, P. A. Günther, S. Hansmann-Menzemer, Arthur M. Hennequin, Louis Henry, D. Hill, S. E. Hollitt, J. Hu, W. Hulsbergen, R. J. Hunter, Mikhail Hushchyn, Brij Kishor Jashal, Christopher Robert Jones, S. Klaver, Konrad Klimaszewski, R. Kopecna, Wojciech Krzemien, Marcin Kucharczyk, R. Lane, F. Lazzari, R. Le Gac, P. Li, J. H. Lopes, M. Lucio Martinez, A. Lupato, Oliver Lupton, X. Lyu, F. Machefert, O. Madejczyk, S. Malde, J. F. Marchand, Saverio Mariani, Carla Marin Benito, D. Martinez Santos, F. Martinez Vidal, R. Matev, Michal Mazurek, B. Mitreska, D. S. Mitzel, M. J. Morello, H. Mu, P. Muzzetto, P. Naik, Matthew D. Needham, N. Neri, Niko Neufeld, N. S. Nolte, Daniel O'Hanlon, A. Oyanguren Campos, M. Pepe Altarelli, S. Petrucci, Marco Petruzzo, L. Pica, Flavio Pisani, A. Piucci, Francesco Polci, A. Poluektov, E. Polycarpo, C. Prouve, Giovanni Punzi, Renato Quagliani, R. I. Rabadan Trejo, M. Ramos Pernas, M. S. Rangel, Fedor Ratnikov, G. Raven, F. Reiss, V. Renaudin, P. Robbe, Artem Ryzhikov, Marco Santimaria, M. Saur, Manuel T. Schiller, Rainer Schwemmer, Barbara Sciascia, A. Solomin, F. Suljik, Nicola Skidmore, Michael D. Sokoloff, P. Spradlin, M. Stahl, S. Stahl, H. Stevens, L. Sun, A. Szabelski, Tomasz Szumlak, M. Szymanski, D. Y. Tou, Giulia Tuci, A. Usachov, Núria Valls Canudas, R. Vazquez Gomez, Stefania Vecchi, Mika Vesterinen, Xavier Vilasís-Cardona, Dorothea Vom Bruch, Z. Wang, Tomasz Wojton, Mark Whitehead, Mike Williams, Mariusz Witek, Y. Xie, A. Xu, H. Yin, Milosz Zdybal, O. Zenaiev, D. Zhang, L. Zhang, X. Zhu, The LHCb Collaboration

Comput. Softw. Big Sci., 2022

Toward an understanding of the properties of neural network approaches for supernovae light curve approximation.

Mariia Demianenko, Konstantin L. Malanchev, Ekaterina Samorodova, Mikhail Sysak, Aleksandr Shiriaev, Denis Derkach, Mikhail Hushchyn

CoRR, 2022

Latent Neural Stochastic Differential Equations for Change Point Detection.

Artem Ryzhikov, Mikhail Hushchyn, Denis Derkach

CoRR, 2022

Supernova Light Curves Approximation based on Neural Network Models.

Mariia Demianenko, Ekaterina Samorodova, Mikhail Sysak, Aleksandr Shiriaev, Konstantin L. Malanchev, Denis Derkach, Mikhail Hushchyn

CoRR, 2022

Towards Reliable Neural Generative Modeling of Detectors.

Lucio Anderlini, Matteo Barbetti, Denis Derkach, Nikita Kazeev, Artem Maevskiy, Sergei Mokhnenko

CoRR, 2022

2021

NFAD: fixing anomaly detection using normalizing flows.

Artem Ryzhikov, Maxim Borisyak, Andrey Ustyuzhanin, Denis Derkach

PeerJ Comput. Sci., 2021

Photometric data-driven classification of Type Ia supernovae in the open Supernova Catalog.

Stanislav Dobryakov, Konstantin L. Malanchev, Denis Derkach, Mikhail Hushchyn

Astron. Comput., 2021

2020

(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets.

Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, Olga Mineeva

J. Mach. Learn. Res., 2020

Online Neural Networks for Change-Point Detection.

Mikhail Hushchyn, Kenenbek Arzymatov, Denis Derkach

CoRR, 2020

Using machine learning to speed up new and upgrade detector studies: a calorimeter case.

Fedor Ratnikov, Denis Derkach, A. Boldyrev, Andrey Shevelev, P. Fakanov, L. Matyushin

CoRR, 2020

Variational Dropout Sparsification for Particle Identification speed-up.

Artem Ryzhikov, Denis Derkach, Mikhail Hushchyn

CoRR, 2020

2019

Normalizing flows for deep anomaly detection.

Artem Ryzhikov, Maxim Borisyak, Andrey Ustyuzhanin, Denis Derkach

CoRR, 2019

$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets.

Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, Olga Mineeva

CoRR, 2019

Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks.

Artem Maevskiy, Denis Derkach, Nikita Kazeev, Andrey Ustyuzhanin, Maksim Artemev, Lucio Anderlini

CoRR, 2019

Cherenkov Detectors Fast Simulation Using Neural Networks.

Denis Derkach, Nikita Kazeev, Fedor Ratnikov, Andrey Ustyuzhanin, Alexandra Volokhova

CoRR, 2019

2017

Deep learning for inferring cause of data anomalies.

V. Azzolini, Maxim Borisyak, Gianluca Cerminara, Denis Derkach, Giovanni Franzoni, F. De Guio, O. Koval, Maurizio Pierini, Adrian Alan Pol, Fedor Ratnikov, Filip Siroky, Andrey Ustyuzhanin, Jean-Roch Vlimant

CoRR, 2017

Numerical optimization for Artificial Retina Algorithm.

Maxim Borisyak, Andrey Ustyuzhanin, Denis Derkach, Mikhail Belous

CoRR, 2017

Towards automation of data quality system for CERN CMS experiment.

Maxim Borisyak, Fedor Ratnikov, Denis Derkach, Andrey Ustyuzhanin

CoRR, 2017

LHCb trigger streams optimization.

Denis Derkach, Nikita Kazeev, R. Neychev, A. Panin, I. Trofimov, Andrey Ustyuzhanin, Mika Vesterinen

CoRR, 2017