77 citations · 79 across the 5 of their papers we have counts for
3 papers · 1 filter
Topological Metric for Unsupervised Embedding Quality Evaluation
Aleksei Shestov, Anton Klenitskiy, Daria Denisova +4
Modern representation learning increasingly relies on unsupervised and self-supervised methods trained on large-scale unlabeled data. While these approaches achieve impressive gene…
LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients
Egor Fadeev, Dzhambulat Mollaev, Aleksei Shestov +6
Learning clients embeddings from sequences of their historic communications is central to financial applications. While large language models (LLMs) offer general world knowledge,…
LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models
Aleksei Shestov, Omar Zoloev, Maksim Makarenko +4
This paper presents LLM4ES, a novel framework that exploits large pre-trained language models (LLMs) to derive user embeddings from event sequences. Event sequences are transformed…