Ge Wang

Computer Vision and Pattern Recognition · IEEE Transactions on Medical Imaging · 2005–2020

A researcher in the Science corpus: 15 papers; trusts 345 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 190 citations into it; #11,501 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 172 citations into it; #31,192 of 35,345 by trust here, #34,213 by citations
• Radiology, Nuclear Medicine and Imaging — 107 citations into it; #127 of 3,265 by trust here, #264 by citations
• Safety, Robustness & Interpretability — 61 citations into it
• Biomedical Engineering — 56 citations into it; #346 of 10,734 by trust here, #1,372 by citations
• Generative Models — 50 citations into it
• Media Technology — 45 citations into it
• Model Architectures — 38 citations into it

Whom Ge Wang trusts overall

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

1. Kurt Hornik
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Simon Osindero
8. Christian Szegedy
9. Ilya Sutskever
10. Kaiming He

Who trusts Ge Wang overall

1. Yanbo Zhang
2. Hongming Shan
3. Mauricio Araya-Polo
4. Zhicheng Zhang
5. Hengyong Yu
6. Yi Zhang
7. Reabal Najjar
8. Qingsong Yang
9. Jonas Adler
10. Yoseob Han

Papers from Ge Wang's perspective

Ordered by how strongly the researchers Ge Wang 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Perspective on Deep Imaging (2016) — 418 citations
• Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss (2017) — 1,435 citations
• Low-Dose X-ray CT Reconstruction via Dictionary Learning (2012) — 691 citations
• Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network (2017) — 1,621 citations
• Low-dose CT via convolutional neural network. (2017) — 688 citations
• Image Reconstruction is a New Frontier of Machine Learning (2018) — 433 citations
• An outlook on x-ray CT research and development. (2008) — 347 citations
• 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (2018) — 392 citations
• Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction (2018) — 379 citations
• LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT (2017) — 408 citations
• On Interpretability of Artificial Neural Networks: A Survey (2020) — 423 citations
• CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (2018) — 479 citations
• A limited-angle CT reconstruction method based on anisotropic TV minimization (2013) — 327 citations
• Compressed Sensing Based Interior Tomography (2009) — 592 citations
• Practical reconstruction method for bioluminescence tomography. (2005) — 319 citations