4 citations · 5 across the 7 of their papers we have counts for
7 papers
The More Popular, The Harder to Forget: Adaptive Popularity for LLM Unlearning
Anna Borisiuk, Andrey Savchenko, Alexander Panchenko +1
Popular facts are memorised more deeply during pretraining and resist removal longer than rare ones, yet existing LLM unlearning methods apply uniform gradient pressure regardless…
Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning
Anna Borisiuk, Andrey Savchenko, Alexander Panchenko +1
Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts are equally forgettable and lar…
HN-MVTS: HyperNetwork-based Multivariate Time Series Forecasting
Andrey Savchenko, Oleg Kachan
Accurate forecasting of multivariate time series data remains a formidable challenge, particularly due to the growing complexity of temporal dependencies in real-world scenarios. W…
HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition
Andrey V. Savchenko
This article presents our results for the eighth Affective Behavior Analysis in-the-Wild (ABAW) competition. We combine facial emotional descriptors extracted by pre-trained models…
Neural Click Models for Recommender Systems
Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev +4
We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models.…
HSEmotion Team at the 7th ABAW Challenge: Multi-Task Learning and Compound Facial Expression Recognition
Andrey V. Savchenko
In this paper, we describe the results of the HSEmotion team in two tasks of the seventh Affective Behavior Analysis in-the-wild (ABAW) competition, namely, multi-task learning for…
HSEmotion Team at the 6th ABAW Competition: Facial Expressions, Valence-Arousal and Emotion Intensity Prediction
Andrey V. Savchenko
This article presents our results for the sixth Affective Behavior Analysis in-the-wild (ABAW) competition. To improve the trustworthiness of facial analysis, we study the possibil…