4 citations · 7 across the 7 of their papers we have counts for
8 papers
VARAN: Variational Inference for Self-Supervised Speech Models Fine-Tuning on Downstream Tasks
Daria Diatlova, Nikita Balagansky, Alexander Varlamov +1
Conventional methods for aggregating layers in fine-tuned self-supervised speech models, such as using the final layer or weighted sum, suffer from information bottlenecks and stat…
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech
Maksim Borisov, Egor Spirin, Daria Diatlova
Current expressive speech synthesis models are constrained by the limited availability of open-source datasets containing diverse nonverbal vocalizations (NVs). In this work, we in…
METR: Image Watermarking with Large Number of Unique Messages
Alexander Varlamov, Daria Diatlova, Egor Spirin
Improvements in diffusion models have boosted the quality of image generation, which has led researchers, companies, and creators to focus on improving watermarking algorithms. Thi…
Adapting WavLM for Speech Emotion Recognition
Daria Diatlova, Anton Udalov, Vitalii Shutov +1
Recently, the usage of speech self-supervised models (SSL) for downstream tasks has been drawing a lot of attention. While large pre-trained models commonly outperform smaller mode…
Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection
Maksim Zubkov, Egor Spirin, Egor Bogomolov +1
Code clones are pairs of code snippets that implement similar functionality. Clone detection is a fundamental branch of automatic source code comprehension, having many application…
Evaluating the Impact of Source Code Parsers on ML4SE Models
Ilya Utkin, Egor Spirin, Egor Bogomolov +1
As researchers and practitioners apply Machine Learning to increasingly more software engineering problems, the approaches they use become more sophisticated. A lot of modern appro…
Assessing Project-Level Fine-Tuning of ML4SE Models
Egor Bogomolov, Sergey Zhuravlev, Egor Spirin +1
Machine Learning for Software Engineering (ML4SE) is an actively growing research area that focuses on methods that help programmers in their work. In order to apply the developed…
PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code
Egor Spirin, Egor Bogomolov, Vladimir Kovalenko +1
The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that resea…