Onur Tasar

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 166 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #30,915 of 35,345 by trust here, #19,439 by citations
• Computer Vision and Pattern Recognition — 32 citations into it; #18,865 of 21,575 by trust here, #12,992 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Meta & Continual Learning — 11 citations into it; #953 of 1,041 by trust here, #711 by citations
• Model Architectures — 7 citations into it
• Optimization & Training — 7 citations into it
• Media Technology — 6 citations into it; #3,138 of 3,621 by trust here, #1,598 by citations

Whom Onur Tasar trusts overall

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

1. Karen Simonyan
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Andrew Zisserman
5. Alex Krizhevsky
6. Kaiming He
7. Yann LeCun
8. Diederik P. Kingma
9. Xavier Glorot
10. Oriol Vinyals

Who trusts Onur Tasar overall

1. Umberto Michieli
2. Marvin Klingner
3. Gaston Lenczner
4. Marc Masana
5. Yingchao Feng
6. Valerio Marsocci
7. Haifeng Li
8. Guanglei Yang
9. GIANG NGUYEN
10. Xialei Liu

Papers from Onur Tasar's perspective

Ordered by how strongly the researchers Onur Tasar 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. ImageNet: A large-scale hierarchical image database
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data (2018) — 144 citations
• Continual Learning for Dense Labeling of Satellite Images (2019) — 7 citations