Nadezhda Chirkova

According to our database, Nadezhda Chirkova authored at least 21 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Zero-shot cross-lingual transfer in instruction tuning of large language model.

Nadezhda Chirkova, Vassilina Nikoulina

CoRR, 2024

Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks.

Nadezhda Chirkova, Vassilina Nikoulina

CoRR, 2024

2023

Empirical study of pretrained multilingual language models for zero-shot cross-lingual generation.

Nadezhda Chirkova, Sheng Liang, Vassilina Nikoulina

CoRR, 2023

CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code.

Nadezhda Chirkova, Sergey Troshin

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Should you marginalize over possible tokenizations?

Nadezhda Chirkova, Germán Kruszewski, Jos Rozen, Marc Dymetman

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023

2022

Parameter-Efficient Finetuning of Transformers for Source Code.

Shamil Ayupov, Nadezhda Chirkova

CoRR, 2022

Probing Pretrained Models of Source Code.

Sergey Troshin, Nadezhda Chirkova

CoRR, 2022

Probing Pretrained Models of Source Codes.

Sergey Troshin, Nadezhda Chirkova

Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2022

2021

Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems.

Evgeny Bobrov, Sergey Troshin, Nadezhda Chirkova, Ekaterina Lobacheva, Sviatoslav Panchenko, Dmitry P. Vetrov, Dmitry Kropotov

CoRR, 2021

On the Memorization Properties of Contrastive Learning.

Ildus Sadrtdinov, Nadezhda Chirkova, Ekaterina Lobacheva

CoRR, 2021

Empirical study of transformers for source code.

Nadezhda Chirkova, Sergey Troshin

Proceedings of the ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021

On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay.

Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova, Andrey Malinin, Dmitry P. Vetrov

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code.

Nadezhda Chirkova, Sergey Troshin

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

On the Embeddings of Variables in Recurrent Neural Networks for Source Code.

Nadezhda Chirkova

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

2020

Neural Code Completion with Anonymized Variable Names.

Nadezhda Chirkova

CoRR, 2020

Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones?

Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry P. Vetrov

CoRR, 2020

On Power Laws in Deep Ensembles.

Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Structured Sparsification of Gated Recurrent Neural Networks.

Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry P. Vetrov

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2018

Bayesian Sparsification of Gated Recurrent Neural Networks.

Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry P. Vetrov

CoRR, 2018

Bayesian Compression for Natural Language Processing.

Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry P. Vetrov

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

2017

Bayesian Sparsification of Recurrent Neural Networks.

Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry P. Vetrov

CoRR, 2017