Bing Han

Electrical and Electronic Engineering · with Roy · 2020

A researcher in the Science corpus: 1 paper; trusts 21 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 14 citations into it; #5,375 of 16,539 by trust here, #13,553 by citations
• Cognitive Neuroscience — 13 citations into it; #4,252 of 10,852 by trust here, #8,899 by citations
• Artificial Intelligence — 10 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Bing Han trusts overall

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

1. Peter U. Diehl
2. Chris L. Baker
3. Vinod Nair
4. Kaiming He
5. Geoffrey E. Hinton
6. Eric Hunsberger
7. Timothée Masquelier
8. Sumit Bam Shrestha
9. Junhaeng Lee
10. Emre O. Neftci

Who trusts Bing Han overall

1. Wei Fang
2. Nitin Rathi
3. Shi-Wee Deng
4. Zhaofei Yu
5. Kaushik Roy
6. Yanqing Chen
7. Yuhang Li
8. Yonghong Tian

Papers from Bing Han's perspective

Ordered by how strongly the researchers Bing Han 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet Large Scale Visual Recognition Challenge
7. Learning Multiple Layers of Features from Tiny Images
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network (2020) — 411 citations