Maryam Mousaarab Najafabadi

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #30,685 of 35,345 by trust here, #9,465 by citations
• Computer Vision and Pattern Recognition — 16 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Computer Networks and Communications — 8 citations into it
• Information Systems — 6 citations into it; #7,473 of 8,771 by trust here, #8,676 by citations
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Maryam Mousaarab Najafabadi trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Quoc V. Le
4. Alex Krizhevsky
5. Ilya Sutskever
6. Tomas Mikolov
7. Yann LeCun
8. Ruslan Salakhutdinov
9. Andrew Y. Ng
10. Diederik P. Kingma

Who trusts Maryam Mousaarab Najafabadi overall

1. Kumar Kshitij Patel
2. Tal Ben-Nun
3. Hussein Issa
4. Alexandra L’Heureux
5. A. Hasan
6. Aymeric Dieuleveut
7. Naveed Akhtar
8. Torsten Hoefler
9. Mohammad Mustafa Taye
10. Andreas Kamilaris

Papers from Maryam Mousaarab Najafabadi's perspective

Ordered by how strongly the researchers Maryam Mousaarab Najafabadi 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep learning applications and challenges in big data analytics (2015) — 2,363 citations
• Large-scale distributed L-BFGS (2017) — 33 citations