43 citations · 76 across the 10 of their papers we have counts for
13 papers
FLOORA: A Human-Aligned Domain-Specific Language Model for Architectural Design
Sahand Rezaei-Shoshtari, Patryk Wozniczka, Shu Ishida +14
Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly. We introduce FLOORA (Floor La…
Learning to Summarize Videos by Contrasting Clips
Ivan Sosnovik, Artem Moskalev, Cees Kaandorp +1
Video summarization aims at choosing parts of a video that narrate a story as close as possible to the original one. Most of the existing video summarization approaches focus on ha…
LieGG: Studying Learned Lie Group Generators
Artem Moskalev, Anna Sepliarskaia, Ivan Sosnovik +1
Symmetries built into a neural network have appeared to be very beneficial for a wide range of tasks as it saves the data to learn them. We depart from the position that when symme…
Contrasting quadratic assignments for set-based representation learning
Artem Moskalev, Ivan Sosnovik, Volker Fischer +1
The standard approach to contrastive learning is to maximize the agreement between different views of the data. The views are ordered in pairs, such that they are either positive,…
Wiggling Weights to Improve the Robustness of Classifiers
Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders
Robustness against unwanted perturbations is an important aspect of deploying neural network classifiers in the real world. Common natural perturbations include noise, saturation,…
PIE: Pseudo-Invertible Encoder
Jan Jetze Beitler, Ivan Sosnovik, Arnold Smeulders
We consider the problem of information compression from high dimensional data. Where many studies consider the problem of compression by non-invertible transformations, we emphasiz…
Two is a crowd: tracking relations in videos
Artem Moskalev, Ivan Sosnovik, Arnold Smeulders
Tracking multiple objects individually differs from tracking groups of related objects. When an object is a part of the group, its trajectory depends on the trajectories of the oth…
Built-in Elastic Transformations for Improved Robustness
Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders
We focus on building robustness in the convolutions of neural visual classifiers, especially against natural perturbations like elastic deformations, occlusions and Gaussian noise.…
DISCO: accurate Discrete Scale Convolutions
Ivan Sosnovik, Artem Moskalev, Arnold Smeulders
Scale is often seen as a given, disturbing factor in many vision tasks. When doing so it is one of the factors why we need more data during learning. In recent work scale equivaria…
Scale Equivariance Improves Siamese Tracking
Ivan Sosnovik, Artem Moskalev, Arnold Smeulders
Siamese trackers turn tracking into similarity estimation between a template and the candidate regions in the frame. Mathematically, one of the key ingredients of success of the si…
Scale-Equivariant Steerable Networks
Ivan Sosnovik, Michał Szmaja, Arnold Smeulders
The effectiveness of Convolutional Neural Networks (CNNs) has been substantially attributed to their built-in property of translation equivariance. However, CNNs do not have embedd…
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha +2
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint dist…
Neural networks for topology optimization
Ivan Sosnovik, Ivan Oseledets
In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty…