Sina Mohseni

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 244 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it; #13,748 of 35,345 by trust here, #19,018 by citations
• Safety, Robustness & Interpretability — 64 citations into it
• Computer Vision and Pattern Recognition — 54 citations into it; #8,960 of 21,575 by trust here, #12,689 by citations
• Safety Research — 22 citations into it
• Human-Computer Interaction — 17 citations into it
• Sociology and Political Science — 16 citations into it
• Information Systems — 14 citations into it
• Information Systems and Management — 14 citations into it; #977 of 3,842 by trust here, #2,689 by citations

Whom Sina Mohseni trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. L. V. D. van der Maaten
4. Robert R. Hoffman
5. Josua Krause
6. Alex Krizhevsky
7. Finale Doshi-Velez
8. Yoshua Bengio
9. Christian Szegedy
10. Mengnan Du

Who trusts Sina Mohseni overall

1. Laura Rieger
2. Lars Kai Hansen
3. Amina Adadi
4. Khansa Rasheed
5. Mohammed Berrada
6. Niloofar Zarei
7. Christoph Molnar
8. Adnan Qayyum
9. Piyawat Lertvittayakumjorn
10. Arun Das

Papers from Sina Mohseni's perspective

Ordered by how strongly the researchers Sina Mohseni 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning (2018) — 63 citations
• A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018) — 808 citations