Wei Ma

Electronic, Optical and Magnetic Materials · with Liu · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 17 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Electronic, Optical and Magnetic Materials — 67 citations into it; #2,415 of 2,886 by trust here, #1,837 by citations
• Atomic and Molecular Physics, and Optics — 50 citations into it
• Biomedical Engineering — 47 citations into it
• Artificial Intelligence — 45 citations into it
• Materials Chemistry — 21 citations into it
• Molecular Biology — 15 citations into it
• AI for Physical Sciences — 13 citations into it
• Computational Theory and Mathematics — 11 citations into it

Whom Wei Ma trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Max Welling
8. Sepp Hochreiter
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Wei Ma overall

1. Yihao Xu
2. Erico Tjoa
3. Zhi Chen
4. Peter R. Wiecha
5. Yongmin Liu
6. Liwei Wang
7. Xianzhe Zhang
8. Zhaocheng Liu
9. Cuntai Guan
10. Hailong Zhou

Papers from Wei Ma's perspective

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

Their papers

• Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials. (2018) — 807 citations
• Deep learning for the design of photonic structures (2020) — 960 citations
• A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures (2020)
• Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy (2019) — 534 citations