Artem Babenko
- Laboratory Head:Faculty of Computer Science / Big Data and Information Retrieval School / Yandex Laboratory
- Artem Babenko has been at HSE University since 2014.
Education and Degrees
- 2017
Candidate of Sciences* (PhD)
- 2012
Master's
Moscow Institute of Physics and Technology
According to the International Standard Classification of Education (ISCED) 2011, Candidate of Sciences belongs to ISCED level 8 - "doctoral or equivalent", together with PhD, DPhil, D.Lit, D.Sc, LL.D, Doctorate or similar. Candidate of Sciences allows its holders to reach the level of the Associate Professor.
Courses (2021/2022)
- Research Seminar ''Internet Data Analysis'' (Master’s programme; Faculty of Computer Science; 2 year, 1, 2 module)Rus
- Past Courses
Courses (2019/2020)
- Bayesian Methods for Machine Learning (Master’s programme; Faculty of Computer Science; 2 year, 1, 2 module)Rus
- Deep Learning (Master’s programme; Faculty of Computer Science; 2 year, 1, 2 module)Rus
- Research Seminar ''Internet Data Analysis'' (Master’s programme; Faculty of Computer Science; 1 year, 1-4 module)Rus
- Research Seminar ''Internet Data Analysis'' (Master’s programme; Faculty of Computer Science; 2 year, 1, 2 module)Rus
20242
- Chapter Егиазарян В. Г., Панферов А. Д., Кузнеделев Д. Д., Babenko A. Extreme Compression of Large Language Models via Additive Quantization, in: Proceedings of the 12th International Conference on Learning Representations (ICLR 2024). ICLR, 2024. P. 1-18. (in press)
- Chapter Стародубцев Н. О., Баранчук Д. А., Федоров А. Р., Babenko A. Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models, in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024. CVPR, 2024. P. 1-23. (in press)
20235
- Chapter Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, Liudmila Prokhorenkova. A critical look at the evaluation of GNNs under heterophily: are we really making progress?, in: Proceedings of the 11th International Conference on Learning Representations (ICLR 2023). ICLR, 2023. doi
- Chapter Oleg Platonov, Denis Kuznedelev, Babenko A., Prokhorenkova L. Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond, in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Curran Associates, Inc., 2023.
- Chapter Bazhenov G., Kuznedelev D., Malinin A., Babenko A., Prokhorenkova L. Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts, in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Curran Associates, Inc., 2023. P. 75567-75594.
- Chapter Anton Voronov, Mikhail Khoroshikh, Artem Babenko, Max Ryabinin. Is This Loss Informative? Faster Text-to-Image Customization by Tracking Objective Dynamics, in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Curran Associates, Inc., 2023. Ch. 1. P. 37491-37510. doi
- Preprint Gorishniy Y., Rubachev Ivan, Kartashev Nikolay, Kotelnikov A., Babenko A., Шлёнский Д. А. TabR: Tabular Deep Learning Meets Nearest Neighbors / arxiv. Series 2307 "14338v2". 2023. doi
20222
- Chapter Baranchuk D., Rubachev I., Voynov A., Khrulkov V., Babenko A. Label-Efficient Semantic Segmentation with Diffusion Models, in: Proceedings of the 10th International Conference on Learning Representations (ICLR 2022). ICLR, 2022.
- Chapter Gorishniy Y., Ivan Rubachev, Babenko A. On Embeddings for Numerical Features in Tabular Deep Learning, in: Thirty-Sixth Conference on Neural Information Processing Systems : NeurIPS 2022. Curran Associates, Inc., 2022. Ch. 1. P. 24991-25004.
20217
- Chapter Khrulkov V., Babenko A., Oseledets I. Functional Space Analysis of Local GAN Convergence, in: Proceedings of the 38th International Conference on Machine Learning (ICML 2021) Vol. 139. PMLR, 2021. P. 5432-5442.
- Chapter Khrulkov V., Mirvakhabova L., Oseledets I., Babenko A. Latent Transformations via NeuralODEs for GAN-based Image Editing, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. , 2021. P. 14428-14437.
- Chapter Cherepkov A., Voynov A., Babenko A. Navigating the GAN Parameter Space for Semantic Image Editing, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. IEEE, 2021. P. 3671-3680.
- Chapter Khrulkov V., Babenko A. Neural Side-by-Side: Predicting Human Preferences for No-Reference Super-Resolution Evaluation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. IEEE, 2021. P. 4988-4997.
- Chapter Voynov A., Morozov S., Babenko A. Object Segmentation Without Labels with Large-Scale Generative Models, in: Proceedings of the 38th International Conference on Machine Learning (ICML 2021) Vol. 139. PMLR, 2021. P. 10596-10606.
- Chapter Morozov S., Voynov A., Babenko A. On Self-Supervised Image Representations for GAN Evaluation, in: Proceedings of the 9th International Conference on Learning Representations (ICLR 2021). ICLR, 2021.. ICLR, 2021. P. 1-17.
- Chapter Gorishniy Y., Rubachev I., Khrulkov V., Babenko A. Revisiting Deep Learning Models for Tabular Data, in: Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, Inc., 2021. P. 18932-18943.
20203
- Chapter Sinitsin A., Plokhotnyuk V., Pyrkin D., Popov S., Babenko A. Editable Neural Networks, in: Proceedings of the 8th International Conference on Learning Representations (ICLR 2020). ICLR, 2020. P. 1-12.
- Chapter Popov S., Morozov S., Babenko A. Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data, in: Proceedings of the 8th International Conference on Learning Representations (ICLR 2020). ICLR, 2020. P. 1-12.
- Chapter Voynov A., Babenko A. Unsupervised Discovery of Interpretable Directions in the GAN Latent Space, in: International Conference on Machine Learning (ICML 2020) Vol. 119. PMLR, 2020. P. 9728-9738.
20193
- Chapter Mazur D., Egiazarian V., Morozov S., Babenko A. Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs, in: Advances in Neural Information Processing Systems 32 (NeurIPS 2019). , 2019. P. 1-11.
- Chapter Баранчук Д. А., Persiyanov D., Sinitsin A., Babenko A. Learning to Route in Similarity Graphs, in: International Conference on Machine Learning (ICML 2019). PMLR, 2019. P. 475-484.
- Chapter Morozov S., Babenko A. Unsupervised neural quantization for compressed-domain similarity search, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2019). IEEE, 2019. P. 3036-3045.
20181
20172
- Chapter Babenko A., Lempitsky V. AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017). Venice : IEEE, 2017. P. 4885-4893. doi
- Chapter Babenko A., Lempitsky V. Product Split Trees, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). Curran Associates, Inc., 2017. P. 2055-2063.
20161
20153
- Chapter Babenko A., Lempitsky V. Aggregating Local Deep Features for Image Retrieval, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2015). Santiago de Chile : IEEE, 2015. P. 1269-1277.
- Article Babenko A., Lempitsky V. The inverted multi-index // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. Vol. 37. No. 6. P. 1247-1260.
- Chapter Babenko A., Lempitsky V. Tree Quantization for Large-Scale Similarity Search and Classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVRP 2015). Curran Associates, Inc., 2015. P. 4240-4248.
20143
- Chapter Babenko A., Lempitsky V. Additive Quantization for Extreme Vector Compression, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014). Columbus : IEEE Computer Society, 2014. P. 931-938.
- Chapter Babenko A., Slesarev A., Chigorin A., Lempitsky V. Neural Codes for Image Retrieval, in: Lecture Notes in Computer Science. Proceedings of the 13th European Conference on Computer Vision (ECCV 2014) Vol. 8689. Part 1. Zürich : Springer, 2014. P. 584-599.
- Article Babenko A. The Inverted Multi-Index // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2014. Vol. PP. No. 99. P. 1.
20121
‘Every Article on NeurIPS Is Considered a Significant Result’
Staff members of the HSE Faculty of Computer Science will present 12 of their works at the 37th Conference and Workshop on Neural Information Processing Systems (NeurIPS), one of the most significant events in the field of artificial intelligence and machine learning. This year it will be held on December 10–16 in New Orleans (USA).
Faculty Submits Ten Papers to NeurIPS 2021
35th Conference on Neural Information Processing Systems (NeurIPS 2021) is one of the world's largest conferences on machine learning and neural networks. It takes place on December 6-14, 2021.
Yandex and HSE University Open Joint Laboratory
The new laboratory will be part of the Faculty of Computer Science. The laboratory will focus on training professional researchers and conducting research in the field of data science.
The faculty presented the results of their research at the largest international machine learning conference NeurIPS
Researchers of the Faculty of Computer Science presented their papers at the annual conference of Neural Information Processing Systems (NeurIPS), which was held from 2 to 8 December 2018 in Montreal, Canada.