Neural networks for topology optimization. - Ivan Sosnovik; Ivan V. Oseledets (CoRR, 2017)

Semi-Conditional Normalizing Flows for Semi-Supervised Learning. - Andrei Atanov; Alexandra Volokhova; Arsenii Ashukha; Ivan Sosnovik; Dmitry P. Vetrov (CoRR, 2019)

Scale-Equivariant Steerable Networks. - Ivan Sosnovik; Michal Szmaja; Arnold W. M. Smeulders (ICLR, 2020)

DISCO: accurate Discrete Scale Convolutions. - Ivan Sosnovik; Artem Moskalev; Arnold W. M. Smeulders (BMVC, 2021)

Relational Prior for Multi-Object Tracking. - Artem Moskalev; Ivan Sosnovik; Arnold W. M. Smeulders (ICCVW, 2021)

How to Transform Kernels for Scale-Convolutions. - Ivan Sosnovik; Artem Moskalev; Arnold W. M. Smeulders (ICCVW, 2021)

Scale Equivariance Improves Siamese Tracking. - Ivan Sosnovik; Artem Moskalev; Arnold W. M. Smeulders (WACV, 2021)

Built-in Elastic Transformations for Improved Robustness. - Sadaf Gulshad; Ivan Sosnovik; Arnold W. M. Smeulders (CoRR, 2021)

Two is a crowd: tracking relations in videos. - Artem Moskalev; Ivan Sosnovik; Arnold W. M. Smeulders (CoRR, 2021)

PIE: Pseudo-Invertible Encoder. - Jan Jetze Beitler; Ivan Sosnovik; Arnold W. M. Smeulders (CoRR, 2021)

Wiggling Weights to Improve the Robustness of Classifiers. - Sadaf Gulshad; Ivan Sosnovik; Arnold W. M. Smeulders (CoRR, 2021)

Contrasting Quadratic Assignments for Set-Based Representation Learning. - Artem Moskalev; Ivan Sosnovik; Volker Fischer; Arnold W. M. Smeulders (ECCV (27), 2022)

LieGG: Studying Learned Lie Group Generators. - Artem Moskalev; Anna Sepliarskaia; Ivan Sosnovik; Arnold W. M. Smeulders (NeurIPS, 2022)

Learning to Summarize Videos by Contrasting Clips. - Ivan Sosnovik; Artem Moskalev; Cees Kaandorp; Arnold W. M. Smeulders (CoRR, 2023)