S. R. Kheradpisheh

Cognitive Neuroscience · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 65 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 68 citations into it; #8,380 of 10,852 by trust here, #5,677 by citations
• Electrical and Electronic Engineering — 55 citations into it; #12,606 of 16,539 by trust here, #9,011 by citations
• Artificial Intelligence — 46 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Cellular and Molecular Neuroscience — 25 citations into it
• Model Architectures — 19 citations into it
• Neuroscience — 12 citations into it
• Signal Processing — 11 citations into it

Whom S. R. Kheradpisheh trusts overall

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

1. Yann LeCun
2. Yoshua Bengio
3. Alex Krizhevsky
4. Peter U. Diehl
5. Geoffrey E. Hinton
6. Simon J. Thorpe
7. Timothée Masquelier
8. Honglak Lee
9. Ilya Sutskever
10. Karen Simonyan

Who trusts S. R. Kheradpisheh overall

1. Michael Pfeiffer
2. Amirhossein Tavanaei
3. Thomas Pfeil
4. Masoud Ghodrati

Papers from S. R. Kheradpisheh's perspective

Ordered by how strongly the researchers S. R. Kheradpisheh 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. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Deep Learning in Spiking Neural Networks (2018) — 1,376 citations
• STDP-based spiking deep neural networks for object recognition (2016) — 458 citations