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Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets
P. 6023–6035.
Ryabinin M., Malinin A., Gales M.
In book
Curran Associates, Inc., 2021.
Golyadkin M., Innokentiy Humonen, Rubanova V. et al., , in: MM '25: Proceedings of the 33rd ACM International Conference on Multimedia.: Association for Computing Machinery (ACM), 2025. P. 12875–12881.
We present the first multimodal dataset MuMMy, for developing research assistants that can interpret Egyptian hieroglyphic texts. It pairs images with Gardiner codes, transliteration, and English translation at two levels of granularity. We also evaluate several deep learning pipelines across OCR, transliteration, and translation tasks, revealing the complexity of the domain and the challenges posed ...
Added: November 8, 2025
Moiseev N., Абрамов И. А., Камакин А. Ю., В кн.: Параллельные вычислительные технологии – XIX всероссийская конференция с международным участием, ПаВТ'2025, г. Москва, 8–10 апреля 2025 г. Короткие статьи и описания плакатов.: Челябинск: Издательский центр ЮУрГУ, 2025. С. 301–301.
In recent years, with the advancement of deep learning and neural network methods, their application in geospatial analysis tasks has become particularly relevant. A key challenge in this field is assessing the state of urban infrastructure, including the classification of buildings by their functional purpose (residential, commercial, governmental, industrial). The use of neural networks significantly ...
Added: September 17, 2025
Fadeeva E., Vashurin R., Tsvigun A. et al., , in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.: Singapore: Association for Computational Linguistics, 2023. P. 446 –461.
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields. However, a significant challenge arises as these models often “hallucinate”, i.e., fabricate facts without providing users an apparent means to discern the veracity of their statements. Uncertainty estimation (UE) methods are one ...
Added: February 17, 2025
Tatiana Sherstinova, Nikolay Mikhaylovskiy, Evgenia Kolpashchikova et al., , in: Proceedings of the 35th Conference of Open Innovations Association FRUCT, 24-26 April 2024, Tampere, FinlandIssue 1.: FRUCT Oy, 2024. P. 253–258.
Contemporary advancements in NLP and neural network techniques are paving the way to enhance and harness traditional linguistic resources and corpora, as well as expand the methods of applying neural networks for complex language material. Thus, a weak point for both theoretical and applied linguistic tasks is the processing of spontaneous everyday speech. Two experiments ...
Added: November 29, 2024
Sobyanin K., Kulikova S., Информатика и автоматизация (Труды СПИИРАН) 2024 Т. 23 № 4 С. 1022–1046
The problem of training deep neural networks on small samples is especially relevant for medical problems. The paper examines the impact of pixel-wise marking of significant objects in the image, over the true class label, on the quality of the classification. To achieve better classification results on small samples, we propose a multitasking architecture -- ...
Added: June 29, 2024
Shuranov E., / Series Computer Science "arxiv.org". 2021.
Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challenging to explain the effect of each information stream on the SER systems. Further, more clarification is required for analysing the impact of ASR's word error rate (WER) on linguistic emotion ...
Added: February 14, 2023
Association for Computational Linguistics, 2022.
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of the works on modeling the uncertainty of deep neural networks evaluate these methods on image classification tasks. Little attention has been paid to UE in natural ...
Added: May 17, 2022
Copenhagen, Denmark: CEUR Workshop Proceedings, 2021.
The second workshop on Crowd Science is organized in conjunction with the 47th International Conference on Very Large Data Bases (VLDB 2021). This workshop is the second in a series of events that has the goal of helping crowdsourcing “transition” from art to science, and tackles the research challenges that we face to make crowdsourcing ...
Added: December 13, 2021
Плетенев С. А., В кн.: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 16–19 июня 2021 г.)Issue 20.: Russian State University for the Humanitie, 2021.
Added: December 13, 2021
Malinin A., Gales M., , in: Advances in Neural Information Processing Systems 32 (NeurIPS 2019).: [б.и.], 2019.
Added: November 1, 2021
Malinin A., Mlodozeniec B., Gales M., , in: Proceedings of the 8th International Conference on Learning Representations (ICLR 2020).: ICLR, 2020.
Added: November 1, 2021
Andrey Malinin, Gales M., , in: Proceedings of the 9th International Conference on Learning Representations (ICLR 2021). ICLR, 2021.: ICLR, 2021. P. 1–31.
Added: November 1, 2021
Myrzin V., Tsoy T., Bai Y. et al., , in: Interactive Collaborative Robotics: 6th International Conference, ICR 2021, St. Petersburg, Russia, September 27–30, 2021, Proceedings.: Springer, 2021. Ch. 12 P. 138–149.
For a large variety of tasks autonomous robots require a robust visual data processing system. This paper presents a new human detection framework that combines rotation-invariant histogram of oriented gradients (RIHOG) features and binarized normed gradients (BING) pre-processing and skin segmentation. For experimental evaluation a new Human body dataset of over 60000 images was constructed ...
Added: October 21, 2021
Sokolov A., Savchenko A., , in: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI).: IEEE, 2021. P. 413–418.
This paper is focused on the finetuning of acoustic models for speaker adaptation goals on a given gender. We pretrained the Transformer baseline model on Librispeech-960 and conducted experiments with finetuning on the gender-specific test subsets. The obtained word error rate (WER) relatively to the baseline is up to 5% and 3% lower on male ...
Added: September 26, 2021
Rohrmanstorfer S., Komarov M. M., Mödritscher F., Mathematics 2021 No. 9 Article 624
With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. ...
Added: September 14, 2021
Meyer J., Rauchenstein L., Eisenberg J., , in: Proceedings of The 12th Language Resources and Evaluation ConferenceVol. 12.: European Language Resources Association (ELRA), 2020. P. 6462–6468.
We describe the creation of the Artie Bias Corpus, an English dataset of expert-validated <audio, transcript> pairs with demographic tags for age, gender, accent. We also release open software which may be used with the Artie Bias Corpus to detect demographic bias in Automatic Speech Recognition systems, and can be extended to other speech technologies. ...
Added: April 20, 2021
Association for Computational Linguistics, 2019.
This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). ...
Added: January 7, 2021
Sokolov A., / Series Computer Science "arxiv.org". 2021.
Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challenging to explain the effect of each information stream on the SER systems. Further, more clarification is required for analysing the impact of ASR's word error rate (WER) on linguistic emotion ...
Added: November 17, 2020