Renata Khasanova

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 2 papers; trusts 251 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #6,241 of 21,575 by trust here, #15,529 by citations
• Artificial Intelligence — 22 citations into it; #22,738 of 35,345 by trust here, #28,481 by citations
• Model Architectures — 15 citations into it
• Graph Learning — 14 citations into it
• Aerospace Engineering — 7 citations into it; #1,231 of 6,421 by trust here, #5,385 by citations
• Media Technology — 7 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Renata Khasanova trusts overall

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

1. Yann LeCun
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Joan Bruna
5. Alex Krizhevsky
6. Yoshua Bengio
7. Donald Ervin Knuth
8. Radford M. Neal
9. Y-Lan Boureau
10. Karen Simonyan

Who trusts Renata Khasanova overall

1. Pascal Frossard
2. Benjamin Coors
3. Yu-Chuan Su
4. Antonio Ortega
5. Christina Heinze-Deml
6. Chao Shang
7. Michaël Defferrard
8. Nathanael Perraudin
9. Risi Kondor
10. Kristen Grauman

Papers from Renata Khasanova's perspective

Ordered by how strongly the researchers Renata Khasanova 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Graph-Based Classification of Omnidirectional Images (2017) — 72 citations
• Graph-based Isometry Invariant Representation Learning (2017) — 55 citations