Vladimir Golkov

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 6 papers; trusts 470 researchers and is trusted by 490 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 125 citations into it; #1,922 of 21,575 by trust here, #2,001 by citations
• Artificial Intelligence — 108 citations into it; #31,113 of 35,345 by trust here, #19,439 by citations
• Optimization & Training — 41 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Model Architectures — 32 citations into it
• Generative Models — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Signal Processing — 10 citations into it

Whom Vladimir Golkov trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Kaiming He
6. Ilya Sutskever
7. Diederik P. Kingma
8. Christian Szegedy
9. Sergey Ioffe
10. Jimmy Ba

Who trusts Vladimir Golkov overall

1. Vincent Fortuin
2. Connor Shorten
3. Yibo Jiang
4. Kazuma Kobayashi
5. Kerstin Hammernik
6. Andrea Vedaldi
7. Yu Li
8. Satya Prakash Singh
9. Piyush Jain
10. Simon Niklaus

Papers from Vladimir Golkov's perspective

Ordered by how strongly the researchers Vladimir Golkov trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• FlowNet: Learning Optical Flow with Convolutional Networks (2015) — 4,664 citations
• Associative Deep Clustering: Training a Classification Network with No Labels (2018) — 132 citations
• Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization (2019) — 351 citations
• Clustering with Deep Learning: Taxonomy and New Methods (2018) — 265 citations
• Regularization for Deep Learning: A Taxonomy (2017) — 371 citations
• q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (2015) — 236 citations