Peter R. Wiecha

Atomic and Molecular Physics, and Optics · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Atomic and Molecular Physics, and Optics — 32 citations into it; #4,022 of 7,135 by trust here, #5,903 by citations
• Electronic, Optical and Magnetic Materials — 32 citations into it
• Biomedical Engineering — 24 citations into it; #5,652 of 10,734 by trust here, #9,032 by citations
• Artificial Intelligence — 23 citations into it
• Electrical and Electronic Engineering — 20 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Aerospace Engineering — 8 citations into it
• Model Architectures — 5 citations into it

Whom Peter R. Wiecha trusts overall

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

1. Kalyanmoy Deb
2. Kaiming He
3. Jiaqi Jiang
4. Yiqiao Tang
5. Arseniy I. Kuznetsov
6. Stanley J. Osher
7. Jakob S. Jensen
8. Andrey Miroshnichenko
9. X. Zhang
10. Yann LeCun

Who trusts Peter R. Wiecha overall

1. Yihao Xu
2. Xianzhe Zhang
3. Yun Raymond Fu

Papers from Peter R. Wiecha's perspective

Ordered by how strongly the researchers Peter R. Wiecha 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures. (2019) — 244 citations
• Deep learning in nano-photonics: inverse design and beyond (2020) — 348 citations