Mehmet Ersin Yümer

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 3 papers; trusts 519 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 56 citations into it; #6,206 of 21,575 by trust here, #13,815 by citations
• Computational Mechanics — 19 citations into it; #1,965 of 10,001 by trust here, #4,749 by citations
• Artificial Intelligence — 14 citations into it
• Computer Graphics and Computer-Aided Design — 11 citations into it; #369 of 1,584 by trust here, #929 by citations
• Aerospace Engineering — 9 citations into it
• Optimization & Training — 6 citations into it
• Generative Models — 5 citations into it
• Geology — 5 citations into it; #457 of 687 by trust here, #665 by citations

Whom Mehmet Ersin Yümer trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Ross B. Girshick
6. Yoshua Bengio
7. David G. Lowe
8. Kaiming He
9. Sergey Ioffe
10. Jitendra Malik

Who trusts Mehmet Ersin Yümer overall

1. Hiroharu Kato
2. Maxim Tatarchenko
3. Jerry Liu
4. Meng Tian
5. Abhishek Sharma
6. Oscar Michel
7. Jhony Kaesemodel Pontes
8. Andrew Brock
9. Tatsuya Harada
10. Chen Kong

Papers from Mehmet Ersin Yümer's perspective

Ordered by how strongly the researchers Mehmet Ersin Yümer 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Semantic Deformation Flows with 3D Convolutional Networks (2016) — 69 citations
• Learning 3D Part Detection from Sparsely Labeled Data (2014) — 12 citations
• Procedural Modeling Using Autoencoder Networks (2015) — 68 citations