Can Li

Electrical and Electronic Engineering · Nature Electronics · 2017–2020

A researcher in the Science corpus: 11 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 192 citations into it
• Cellular and Molecular Neuroscience — 95 citations into it

Whom Can Li trusts overall

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

1. Yann LeCun
2. Paul J. Werbos
3. Geoffrey E. Hinton
4. Michael C. Mozer
5. Dmitri B. Strukov
6. J. Joshua Yang
7. J. Urgen Schmidhuber
8. David E. Rumelhart
9. Yoshua Bengio
10. Sepp Hochreiter

Who trusts Can Li overall

1. Peng Yao
2. Bin Gao
3. Zhongrui Wang
4. Wenqiang Zhang
5. Jianshi Tang
6. Qiangfei Xia
7. Huaqiang Wu

Papers from Can Li's perspective

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

Their papers

• Analogue signal and image processing with large memristor crossbars (2017) — 1,118 citations
• Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension (2018) — 444 citations
• A novel true random number generator based on a stochastic diffusive memristor (2017) — 390 citations
• Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine (2018) — 676 citations
• Fully memristive neural networks for pattern classification with unsupervised learning (2018) — 993 citations
• Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks (2020) — 378 citations
• Three-dimensional memristor circuits as complex neural networks (2020) — 313 citations
• Reinforcement learning with analogue memristor arrays (2019) — 334 citations
• Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications (2018) — 309 citations
• Efficient and self-adaptive in-situ learning in multilayer memristor neural networks (2018) — 828 citations
• Long short-term memory networks in memristor crossbar arrays (2018) — 368 citations