Daniil Selikhanovych

According to our database, Daniil Selikhanovych authored at least 10 papers between 2019 and 2024.

Collaborative distances:

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

2024

A3D: Does Diffusion Dream about 3D Alignment?

Savva Ignatyev, Nina Konovalova, Daniil Selikhanovych, Nikolay Patakin, Oleg Voynov, Dmitry Senushkin, Alexander Filippov, Anton Konushin, Peter Wonka, Evgeny Burnaev

CoRR, 2024

Adversarial Schrödinger Bridge Matching.

Nikita Gushchin, Daniil Selikhanovych, Sergei Kholkin, Evgeny Burnaev, Alexander Korotin

CoRR, 2024

2023

NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion.

Savva Ignatyev, Daniil Selikhanovych, Oleg Voynov, Yiqun Wang, Peter Wonka, Stamatios Lefkimmiatis, Evgeny Burnaev

CoRR, 2023

Extremal Domain Translation with Neural Optimal Transport.

Milena Gazdieva, Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Kernel Neural Optimal Transport.

Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Neural Optimal Transport.

Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2021

DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution.

Iaroslav Koshelev, Daniil Selikhanovych, Stamatios Lefkimmiatis

CoRR, 2021

Ex²MCMC: Sampling through Exploration Exploitation.

Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines

CoRR, 2021

2019

Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization.

Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, César A. Uribe

Proceedings of the Conference on Learning Theory, 2019

Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives.

Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford

Proceedings of the Conference on Learning Theory, 2019