Pouya Bashivan

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 6 papers; trusts 312 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 92 citations into it
• Artificial Intelligence — 74 citations into it; #31,415 of 35,345 by trust here, #16,166 by citations
• Cognitive Neuroscience — 60 citations into it; #2,631 of 10,852 by trust here, #1,946 by citations
• Model Architectures — 42 citations into it
• Optimization & Training — 18 citations into it
• Neuroscience — 17 citations into it
• Electrical and Electronic Engineering — 9 citations into it
• Safety, Robustness & Interpretability — 9 citations into it

Whom Pouya Bashivan trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Diederik P. Kingma
5. Karen Simonyan
6. Kaiming He
7. Yann LeCun
8. Matthew D. Zeiler
9. Ilya Sutskever
10. Barret Zoph

Who trusts Pouya Bashivan overall

1. Grace W. Lindsay
2. Guangyu Robert Yang
3. Martin Schrimpf
4. Andrew M. Saxe
5. Yousef Rezaei Tabar
6. Chengxu Zhuang
7. Elnaz Lashgari
8. Jianping Gou
9. Pierre Thodoroff
10. Hauke Dose

Papers from Pouya Bashivan's perspective

Ordered by how strongly the researchers Pouya Bashivan 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks (2018) — 355 citations
• Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs (2019) — 333 citations
• Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? (2018) — 725 citations
• Teacher Guided Architecture Search (2018) — 29 citations
• Neural population control via deep image synthesis (2018) — 464 citations
• Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks (2015) — 765 citations