Aran Nayebi

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 153 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 88 citations into it
• Artificial Intelligence — 64 citations into it; #12,324 of 35,345 by trust here, #19,439 by citations
• Cognitive Neuroscience — 47 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Model Architectures — 20 citations into it
• Neuroscience — 13 citations into it; #175 of 487 by trust here, #379 by citations
• Optimization & Training — 9 citations into it
• Safety, Robustness & Interpretability — 9 citations into it; #844 of 2,251 by trust here, #1,907 by citations

Whom Aran Nayebi trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Nicolas Papernot
4. Ian J. Goodfellow
5. Alex Krizhevsky
6. Shun-ichi Amari
7. Christian Szegedy
8. Kaiming He
9. Yoshua Bengio
10. Diederik P. Kingma

Who trusts Aran Nayebi overall

1. David Stutz
2. Wieland Brendel
3. Andrew Slavin Ross
4. Matthias Hein
5. Matthias Bethge
6. Bernt Schiele
7. Finale Doshi-Velez
8. Naveed Akhtar
9. A. Serban
10. Aditi Raghunathan

Papers from Aran Nayebi's perspective

Ordered by how strongly the researchers Aran Nayebi 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning Physical Graph Representations from Visual Scenes (2020) — 88 citations
• Biologically inspired protection of deep networks from adversarial attacks (2017) — 121 citations
• Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs (2019) — 333 citations
• Unsupervised neural network models of the ventral visual stream (2020) — 414 citations