15 citations · 19 across the 12 of their papers we have counts for
16 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…
ERank in Latent Space as an Image-Complexity and Richness Measure
Maksim Smirnov, Grigory Kononov, Anastasiia Linich +2
We propose the effective rank (ERank) of the channel covariance of an image's deep feature map as a per-sample, label-free measure of visual richness, computed from a single forwar…
Bug or Feature: Weight Drift and Activation Sparsity
Egor Shvetsov, Aleksandr Serkov, Shokorov Viacheslav +3
The design of modern neural architectures has converged through incremental empirical choices, yet the mechanisms governing their training dynamics remain only partially understood…
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…
Geometric Metrics and LLMs: What They Measure and When They Work
Viacheslav Yusupov, Anna Antipina, Ameliia Alaeva +6
We present a systematic stress-test of geometric metrics for LLM evaluation. Rank-based geometric properties of internal representations have shown promise as reference-free qualit…
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva +7
The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for we…
Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
Maxim Zhelnin, Dmitry Redko, Daniil Volkov +8
Sequential recommendations (SR) with transformer-based architectures are widely adopted in real-world applications, where SR models require frequent retraining to adapt to ever-cha…
Token Homogenization under Positional Bias
Viacheslav Yusupov, Danil Maksimov, Ameliia Alaeva +9
This paper investigates token homogenization - the convergence of token representations toward uniformity across transformer layers and its relationship to positional bias in large…
From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction
Egor Maximov, Yulia Kuzkina, Azamat Kanametov +4
As large language models (LLMs) grow in size, efficient compression techniques like quantization and sparsification are critical. While quantization maintains performance with redu…
Beyond Early-Token Bias: Model-Specific and Language-Specific Position Effects in Multilingual LLMs
Mikhail Menschikov, Alexander Kharitonov, Maiia Kotyga +5
Large Language Models (LLMs) exhibit position bias systematically underweighting information based on its location in the context but how this bias varies across languages and mode…
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models
Artyom Kharinaev, Viktor Moskvoretskii, Egor Shvetsov +3
Large Language Models (LLMs) are powerful tools for modern applications, but their computational demands limit accessibility. Quantization offers efficiency gains, yet its impact o…
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…
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
Maxim Zhelnin, Viktor Moskvoretskii, Egor Shvetsov +4
Parameter Efficient Fine-Tuning (PEFT) methods have gained popularity and democratized the usage of Large Language Models (LLMs). Recent studies have shown that a small subset of w…
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…