Vladimir Makarenkov
Artificial Intelligence · 2002–2020
A researcher in the Science corpus: 4 papers; trusts 23 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 139 citations into it
- Probabilistic & Bayesian Methods — 54 citations into it
- Molecular Biology — 13 citations into it; #12,939 of 17,773 by trust here, #14,509 by citations
- Genetics — 8 citations into it; #4,683 of 6,854 by trust here, #5,057 by citations
Whom Vladimir Makarenkov trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Vladimir Makarenkov overall
- Pierre Legendre
- R. M. Pinto-Coelho
- Jagoba Malumbres‐Olarte
- Harry John Betteley Birks
- H. Wagner
- Alexander Suh
- B. Pinel‐Alloul
- C. Vink
- Niko Balkenhol
- G. Méthot
Papers from Vladimir Makarenkov's perspective
Ordered by how strongly the researchers Vladimir Makarenkov trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Auto-Encoding Variational Bayes (International Conference on Learning Representations)
- Dropout: a simple way to prevent neural networks from overfitting
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Intriguing properties of neural networks
- Fully convolutional networks for semantic segmentation
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
Their papers
- T-REX: a web server for inferring, validating and visualizing phylogenetic trees and networks (2012) — 494 citations
- Nonlinear redundancy analysis and canonical correspondence analysis based on polynomial regression (2002) — 161 citations
- A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges (2020) — 2,678 citations
- A new machine learning technique for an accurate diagnosis of coronary artery disease (2019) — 299 citations