15 citations · 19 across the 5 of their papers we have counts for
6 papers
Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective
Dmitry Osin, Egor Surkov, Petr Mokrov +5
Event sequences are chains of random events, each described by heterogeneous (numerical and categorical) features. The problem of event sequence forecasting occurs in finance, heal…
How to model Human Actions distribution with Event Sequence Data
Egor Surkov, Dmitry Osin, Evgeny Burnaev +1
This paper studies forecasting of the future distribution of events in human action sequences, a task essential in domains like retail, finance, healthcare, and recommendation syst…
EBES: Easy Benchmarking for Event Sequences
Dmitry Osin, Igor Udovichenko, Viktor Moskvoretskii +2
Event Sequences (EvS) refer to sequential data characterized by irregular sampling intervals and a mix of categorical and numerical features. Accurate classification of these seque…
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…
MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences
Viktor Moskvoretskii, Dmitry Osin, Egor Shvetsov +5
This study explores the application of self-supervised learning techniques for event sequences. It is a key modality in various applications such as banking, e-commerce, and health…
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise
Egor Shvetsov, Dmitry Osin, Alexey Zaytsev +4
There is a constant need for high-performing and computationally efficient neural network models for image super-resolution: computationally efficient models can be used via low-ca…