27 citations · 34 across the 9 of their papers we have counts for
3 papers · 1 filter
Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
Ilia Revin, Leon Strelkov, Vadim A. Potemkin +2
There are many critical challenges in optimizing neural network models, including distributed computing, compression techniques, and efficient training, regardless of their applica…
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…