15 citations · 80 across the 20 of their papers we have counts for
12 papers · 1 filter
RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks
Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov +2
Topological methods for comparing weighted graphs are valuable in various learning tasks but often suffer from computational inefficiency on large datasets. We introduce RTD-Lite,…
SeqNAS: Neural Architecture Search for Event Sequence Classification
Igor Udovichenko, Egor Shvetsov, Denis Divitsky +6
Neural Architecture Search (NAS) methods are widely used in various industries to obtain high quality taskspecific solutions with minimal human intervention. Event Sequences find w…
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…
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…
Manifold Topology Divergence: a Framework for Comparing Data Manifolds
Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov +4
We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, giv…
Loss Barcode: A Topological Measure of Escapability in Loss Landscapes
Serguei Barannikov, Daria Voronkova, Alexander Mironenko +4
Neural network training is commonly based on SGD. However, the understanding of SGD's ability to converge to good local minima, given the non-convex nature of loss functions and th…
Optimizing the Neural Architecture of Reinforcement Learning Agents
N. Mazyavkina, S. Moustafa, I. Trofimov +1
Reinforcement learning (RL) enjoyed significant progress over the last years. One of the most important steps forward was the wide application of neural networks. However, architec…
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing
Nikita Klyuchnikov, Ilya Trofimov, Ekaterina Artemova +3
Neural Architecture Search (NAS) is a promising and rapidly evolving research area. Training a large number of neural networks requires an exceptional amount of computational power…
Multi-fidelity Neural Architecture Search with Knowledge Distillation
Ilya Trofimov, Nikita Klyuchnikov, Mikhail Salnikov +2
Neural architecture search (NAS) targets at finding the optimal architecture of a neural network for a problem or a family of problems. Evaluations of neural architectures are very…
Using Neural Networks for Click Prediction of Sponsored Search
Afroze Ibrahim Baqapuri, Ilya Trofimov
Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). click-through-rate (CTR) estimation plays a crucial role for ads…