Ozan Caglayan

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 365 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it; #13,944 of 35,345 by trust here, #8,439 by citations
• Computer Vision and Pattern Recognition — 88 citations into it; #10,689 of 21,575 by trust here, #8,296 by citations
• Natural Language Processing — 84 citations into it
• Multimodal Learning — 37 citations into it; #565 of 1,211 by trust here, #671 by citations
• Model Architectures — 35 citations into it
• Evaluation & Benchmarks — 33 citations into it; #3,099 of 3,147 by trust here, #2,244 by citations
• Optimization & Training — 22 citations into it
• Machine Translation — 13 citations into it; #125 of 292 by trust here, #140 by citations

Whom Ozan Caglayan trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kaiming He
7. Kyunghyun Cho
8. Dzmitry Bahdanau
9. Karen Simonyan
10. Jimmy Ba

Who trusts Ozan Caglayan overall

1. Desmond Elliott
2. Pranava Swaroop Madhyastha
3. Xin Eric Wang
4. Loïc Barrault
5. Lucia Specia
6. Tejas Srinivasan
7. Jason Lee
8. Rachel Bawden
9. Bei Li
10. Zhuosheng Zhang

Papers from Ozan Caglayan's perspective

Ordered by how strongly the researchers Ozan Caglayan 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Multimodal Attention for Neural Machine Translation (2016) — 80 citations
• Probing the Need for Visual Context in Multimodal Machine Translation (2019) — 158 citations
• How2: A Large-scale Dataset for Multimodal Language Understanding (2018) — 326 citations
• LIUM-CVC Submissions for WMT17 Multimodal Translation Task (2017) — 71 citations
• Does Multimodality Help Human and Machine for Translation and Image Captioning? (2016) — 87 citations
• LIUM Machine Translation Systems for WMT17 News Translation Task (2017) — 15 citations