Mohammad Sadegh Norouzzadeh

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 83 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #33,316 of 35,345 by trust here, #33,294 by citations
• Computer Vision and Pattern Recognition — 48 citations into it
• Ecology — 22 citations into it
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Media Technology — 9 citations into it
• Social Psychology — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Mohammad Sadegh Norouzzadeh trusts overall

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

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

Who trusts Mohammad Sadegh Norouzzadeh overall

1. Maximilian Pichler
2. Sara Beery
3. Sylvain Christin
4. Jeff Clune
5. N. Sinatra
6. Marco Willi
7. Florian Hartig
8. Andrea Soltoggio
9. É. Hervet

Papers from Mohammad Sadegh Norouzzadeh's perspective

Ordered by how strongly the researchers Mohammad Sadegh Norouzzadeh 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. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning (2017) — 1,085 citations
• Neuromodulation Improves the Evolution of Forward Models (2016) — 16 citations
• Machine learning to classify animal species in camera trap images: applications in ecology (2018) — 439 citations
• Automatically identifying wild animals in camera trap images with deep learning (2017) — 51 citations