Kirill O. Neklyudov
- Lecturer, Postgraduate Student:Faculty of Computer Science / Big Data and Information Retrieval School
- Research Fellow:Faculty of Computer Science / Big Data and Information Retrieval School / Samsung-HSE Laboratory
- Kirill O. Neklyudov has been at HSE since 2016.
Continuing education / Professional retraining / Internships / Study abroad experience
Yandex School of Data Analysis (2014-2016), big data department
Intern, Yandex (november 2013 - june 2014)
Postgraduate Studies
3rd year of study
Approved topic of thesis: Differentiable architectures for neural networks
Academic Supervisor: Vetrov, Dmitry
Courses (2018/2019)
- Bayesian Methods for Machine Learning (Bachelor’s programme; Faculty of Computer Science; programme "Applied Mathematics and Information Science"; 4 year, 1, 2 module)Rus
- Machine Learning 1 (Bachelor’s programme; Faculty of Computer Science; programme "Applied Mathematics and Information Science"; 3 year, 1, 2 module)Rus
Other publications2
- Chapter Ashukha A., Vetrov D., Molchanov D., Neklyudov K. O., Atanov A. Uncertainty Estimation via Stochastic Batch Normalization, in: Workshop of the 6th International Conference on Learning Representations (ICLR).. , 2018.
- Chapter Neklyudov K. O., Molchanov D., Ashukha A., Vetrov D. Structured Bayesian Pruning via Log-Normal Multiplicative Noise, in: Advances in Neural Information Processing Systems 30 (NIPS 2017). Montreal : Curran Associates, 2017. P. 6776-6785.
The faculty members will present their research on ICLR and AISTATS conferences
One paper will be presented at AISTATS (Japan, April 2019) and three papers will be presented at ICLR (USA, May 2019).
How to Adjust a Smaller Size Neural Network without Quality Loss
Staff members of the HSE Faculty of Computer Science recently presented their papers at the biggest international conference on machine learning, Neural Information Processing Systems (NIPS)’.
How to Adjust a Smaller Size Neural Network without Quality Loss
Staff members of the HSE Faculty of Computer Science recently presented their papers at the biggest international conference on machine learning, Neural Information Processing Systems (NIPS)’.
