7 papers
When Losses Align: Gradient-Based Composite Loss Weighting for Efficient Pretraining
Ivan Karpukhin, Andrey Savchenko
Modern deep models are often pretrained on large-scale data with missing labels using composite objectives, where the relative weights of multiple loss terms act as hyperparameters…
Embedding-Aware Feature Discovery: Bridging Latent Representations and Interpretable Features in Event Sequences
Artem Sakhno, Ivan Sergeev, Alexey Shestov +5
Industrial financial systems operate on temporal event sequences such as transactions, user actions, and system logs. While recent research emphasizes representation learning and l…
Financial Transaction Retrieval and Contextual Evidence for Knowledge-Grounded Reasoning
Artem Sakhno, Daniil Tomilov, Yuliana Shakhvalieva +5
Nowadays, success of financial organizations heavily depends on their ability to process digital traces generated by their clients, e.g., transaction histories, gathered from vario…
Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching
Ivan Karpukhin, Andrey Savchenko
Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often e…
HT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokens
Ivan Karpukhin, Andrey Savchenko
Deep learning has achieved remarkable success in modeling sequential data, including event sequences, temporal point processes, and irregular time series. Recently, transformers ha…
HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?
Ivan Karpukhin, Foma Shipilov, Andrey Savchenko
Forecasting multiple future events within a given time horizon is essential for applications in finance, retail, social networks, and healthcare. Marked Temporal Point Processes (M…