Mert Bulent Sariyildiz

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 955 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 212 citations into it; #8,701 of 21,575 by trust here, #5,620 by citations
• Artificial Intelligence — 184 citations into it; #14,052 of 35,345 by trust here, #7,797 by citations
• Representation & Self-Supervised Learning — 136 citations into it; #2,404 of 3,145 by trust here, #1,127 by citations
• Evaluation & Benchmarks — 31 citations into it
• Multimodal Learning — 23 citations into it; #824 of 1,211 by trust here, #770 by citations
• Radiology, Nuclear Medicine and Imaging — 23 citations into it
• Optimization & Training — 17 citations into it
• Natural Language Processing — 14 citations into it

Whom Mert Bulent Sariyildiz trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Ross B. Girshick
6. Diederik P. Kingma
7. Karen Simonyan
8. Ilya Sutskever
9. Yann LeCun
10. Andrew Zisserman

Who trusts Mert Bulent Sariyildiz overall

1. Karan Desai
2. Bo Zhao
3. Zongyan Han
4. Yiwu Zhong
5. Zhangxuan Gu
6. Hieu Pham
7. Justin Johnson
8. Benjamin Devillers
9. Ali Cheraghian
10. Yuxuan Zhang

Papers from Mert Bulent Sariyildiz's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning Visual Representations with Caption Annotations (2020) — 173 citations
• Hard Negative Mixing for Contrastive Learning (2020) — 775 citations
• Concept Generalization in Visual Representation Learning (2020) — 54 citations
• Gradient Matching Generative Networks for Zero-Shot Learning (2019) — 154 citations