Guangyu Robert Yang

Cognitive Neuroscience · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 442 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 160 citations into it; #5,575 of 10,852 by trust here, #4,022 by citations
• Artificial Intelligence — 127 citations into it
• Computer Vision and Pattern Recognition — 66 citations into it
• Model Architectures — 63 citations into it
• Electrical and Electronic Engineering — 52 citations into it
• Cellular and Molecular Neuroscience — 49 citations into it
• Neuroscience — 48 citations into it
• Optimization & Training — 33 citations into it

Whom Guangyu Robert Yang trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Alex Krizhevsky
6. Yann LeCun
7. Jimmy Ba
8. Ilya Sutskever
9. Richard S. Sutton
10. Ronald J. Williams

Who trusts Guangyu Robert Yang overall

1. Benjamin Ehret
2. Saurabh Vyas
3. Mehrdad Jazayeri
4. Aditya Mogadala
5. Ta-Chu Kao
6. Lea Duncker
7. Christian Henning
8. Matthew D. Golub
9. Krishna V. Shenoy
10. Sergey I. Nikolenko

Papers from Guangyu Robert Yang's perspective

Ordered by how strongly the researchers Guangyu Robert Yang 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework (2016) — 300 citations
• Artificial Neural Networks for Neuroscientists: A Primer (2020) — 325 citations
• Task representations in neural networks trained to perform many cognitive tasks (2019) — 606 citations
• A dataset and architecture for visual reasoning with a working memory (2018) — 56 citations
• Reward-based training of recurrent neural networks for cognitive and value-based tasks (2016) — 176 citations