Saeid Motiian

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 831 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 138 citations into it; #5,962 of 35,345 by trust here, #6,103 by citations
• Computer Vision and Pattern Recognition — 134 citations into it; #6,984 of 21,575 by trust here, #6,371 by citations
• Representation & Self-Supervised Learning — 71 citations into it; #880 of 3,145 by trust here, #893 by citations
• Evaluation & Benchmarks — 19 citations into it
• Signal Processing — 16 citations into it
• Generative Models — 12 citations into it
• Optimization & Training — 12 citations into it
• Cancer Research — 11 citations into it

Whom Saeid Motiian trusts overall

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

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

Who trusts Saeid Motiian overall

1. Mohammad Rostami
2. Kaiyang Zhou
3. Nanqing Dong
4. Han-Jia Ye
5. Haohan Wang
6. Eric P. Xing
7. Shaoan Xie
8. Mei Wang
9. Gianfranco Doretto
10. Fengchun Qiao

Papers from Saeid Motiian's perspective

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

Their papers

• Unified Deep Supervised Domain Adaptation and Generalization (2017) — 889 citations
• Information Bottleneck Learning Using Privileged Information for Visual Recognition (2016) — 54 citations
• Few-Shot Adversarial Domain Adaptation (2017) — 435 citations
• Information Bottleneck Domain Adaptation with Privileged Information for Visual Recognition (2016) — 24 citations