9 citations · 12 across the 5 of their papers we have counts for
5 papers
Disentanglement Learning via Topology
Nikita Balabin, Daria Voronkova, Ilya Trofimov +2
We propose TopDis (Topological Disentanglement), a method for learning disentangled representations via adding a multi-scale topological loss term. Disentanglement is a crucial pro…
Learning Topology-Preserving Data Representations
Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii +3
We propose a method for learning topology-preserving data representations (dimensionality reduction). The method aims to provide topological similarity between the data manifold an…
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting
Timofey Grigoryev, Polina Verezemskaya, Mikhail Krinitskiy +9
Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe. While ocean-ice numerical models are…
Transfer learning for ensembles: reducing computation time and keeping the diversity
Ilya Shashkov, Nikita Balabin, Evgeny Burnaev +1
Transferring a deep neural network trained on one problem to another requires only a small amount of data and little additional computation time. The same behaviour holds for ensem…
Representation Topology Divergence: A Method for Comparing Neural Network Representations
Serguei Barannikov, Ilya Trofimov, Nikita Balabin +1
Comparison of data representations is a complex multi-aspect problem that has not enjoyed a complete solution yet. We propose a method for comparing two data representations. We in…