5 citations · 7 across the 5 of their papers we have counts for
5 papers
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes
Maksim Golyadkin, Vitaliy Pozdnyakov, Leonid Zhukov +1
Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to…
Dealing with Sparse Rewards Using Graph Neural Networks
Matvey Gerasyov, Ilya Makarov
Deep reinforcement learning in partially observable environments is a difficult task in itself, and can be further complicated by a sparse reward signal. Most tasks involving navig…
Temporal Graph Network Embedding with Causal Anonymous Walks Representations
Ilya Makarov, Andrey Savchenko, Arseny Korovko +4
Many tasks in graph machine learning, such as link prediction and node classification, are typically solved by using representation learning, in which each node or edge in the netw…
Epidemic modelling of multiple virus strains: a case study of SARS-CoV-2 B.1.1.7 in Moscow
Boris Tseytlin, Ilya Makarov
During a long-running pandemic a pathogen can mutate, producing new strains with different epidemiological parameters. Existing approaches to epidemic modelling only consider one v…
Hotel Recognition via Latent Image Embedding
Boris Tseytlin, Ilya Makarov
We approach the problem of hotel recognition with deep metric learning. We overview the existing approaches and propose a modification to Contrastive loss called Contrastive-Triple…