| Empirical Study of Transformers for Source Code N Chirkova, S Troshin ESEC/FSE 2021: ACM Joint European Software Engineering Conference and …, 2020 | 59 | 2020 |
| On Power Laws in Deep Ensembles E Lobacheva, N Chirkova, M Kodryan, D Vetrov NeurIPS 2020: Advances in Neural Information Processing Systems, 2020 …, 2020 | 45 | 2020 |
| Additive regularization for hierarchical multimodal topic modeling NA Chirkova, KV Vorontsov Journal Machine Learning and Data Analysis 2 (2), 187-200, 2016 | 35 | 2016 |
| Probing pretrained models of source code S Troshin, N Chirkova BlackboxNLP Workshop as EMNLP 2022, 2022 | 27 | 2022 |
| On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay E Lobacheva, M Kodryan, N Chirkova, A Malinin, DP Vetrov NeurIPS 2021: Advances in Neural Information Processing Systems 34, 2021 | 23 | 2021 |
| Bayesian sparsification of recurrent neural networks E Lobacheva, N Chirkova, D Vetrov ICML Workshop on Learning to Generate Natural Language, 2017 | 19 | 2017 |
| Bayesian compression for natural language processing N Chirkova, E Lobacheva, D Vetrov EMNLP 2018: 2018 Conference on Empirical Methods in Natural Language Processing, 2018 | 16 | 2018 |
| A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code N Chirkova, S Troshin NAACL 2021: Annual Conference of the North American Chapter of the …, 2020 | 12 | 2020 |
| Parameter-Efficient Finetuning of Transformers for Source Code S Ayupov, N Chirkova Workshop on Efficient Natural Language Processing at NeurIPS 2022, 2022 | 8 | 2022 |
| Deep ensembles on a fixed memory budget: One wide network or several thinner ones? N Chirkova, E Lobacheva, D Vetrov arXiv preprint arXiv:2005.07292, 2020 | 8 | 2020 |
| Structured Sparsification of Gated Recurrent Neural Networks E Lobacheva, N Chirkova, A Markovich, D Vetrov NeurIPS Workshop on Context and Compositionality in Biological and …, 2019 | 8 | 2019 |
| CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code N Chirkova, S Troshin ICLR 2023, 2023 | 6 | 2023 |
| On the Embeddings of Variables in Recurrent Neural Networks for Source Code N Chirkova NAACL 2021: 2021 Conference of the North American Chapter of the Association …, 2021 | 6 | 2021 |
| Bayesian Sparsification of Gated Recurrent Neural Networks E Lobacheva, N Chirkova, D Vetrov NeurIPS Workshop on Compact Deep Neural Network Representation with …, 2018 | 4 | 2018 |
| Should you marginalize over possible tokenizations? N Chirkova, G Kruszewski, J Rozen, M Dymetman ACL 2023: 61st Annual Meeting of the Association for Computational Linguistics, 2023 | 2 | 2023 |
| On the Memorization Properties of Contrastive Learning I Sadrtdinov, N Chirkova, E Lobacheva ICML Workshop on Overparameterization: Pitfalls & Opportunities, 2021, 2021 | 1 | 2021 |
| Zero-shot cross-lingual transfer in instruction tuning of large language models N Chirkova, V Nikoulina arXiv preprint arXiv:2402.14778, 2024 | | 2024 |
| Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks N Chirkova, V Nikoulina NAACL 2024, 2024 | | 2024 |
| Empirical study of pretrained multilingual language models for zero-shot cross-lingual generation N Chirkova, S Liang, V Nikoulina arXiv preprint arXiv:2310.09917, 2023 | | 2023 |
| Electronic apparatus for compressing recurrent neural network and method thereof EM Lobacheva, NA Chirkova, DP Vetrov US Patent 11,568,237, 2023 | | 2023 |
Ekaterina LobachevaHSE University / bayesgroup.ruПодтвержден адрес электронной почты в домене hse.ru
