Ievgen Redko
Artificial Intelligence · 2016–2021
A researcher in the Science corpus: 2 papers; trusts 12 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 19 citations into it; #13,018 of 35,345 by trust here, #21,878 by citations
- Computer Vision and Pattern Recognition — 10 citations into it; #8,566 of 21,575 by trust here, #14,478 by citations
- Applied Mathematics — 9 citations into it
- Representation & Self-Supervised Learning — 9 citations into it; #1,467 of 3,145 by trust here, #2,454 by citations
- Optimization & Training — 6 citations into it
- Mathematical Optimization — 5 citations into it; #242 of 422 by trust here, #247 by citations
- Signal Processing — 5 citations into it
- Statistics and Probability — 5 citations into it
Whom Ievgen Redko trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Marco Cuturi
- Yishay Mansour
- Cédric Villani
- Nicolas Courty
- François Bolley
- Mehryar Mohri
- Julien Rabin
- Shai Ben-David
- Arnaud Guillin
- Arnaud Doucet
Who trusts Ievgen Redko overall
- Mohammad Rostami
- Soheil Kolouri
- Yuchen Zhang
- Alex Gabourie
- Aram Galstyan
- Eric Eaton
- Kyungnam Kim
- Praveen K. Pilly
- Mingsheng Long
- Jian Shen
Papers from Ievgen Redko's perspective
Ordered by how strongly the researchers Ievgen Redko trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- A Survey on Transfer Learning
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Wasserstein Generative Adversarial Networks
- Optimal Transport: Old and New
- Analysis of Representations for Domain Adaptation
- GENERATIVE ADVERSARIAL NETS
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
Their papers
- Theoretical Analysis of Domain Adaptation with Optimal Transport (2016) — 222 citations
- POT: Python Optimal Transport (2021) — 1,118 citations