Jianwei Ma
Computer Vision and Pattern Recognition · 2009–2021
A researcher in the Science corpus: 9 papers; trusts 667 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 124 citations into it; #6,235 of 21,575 by trust here, #12,419 by citations
- Geophysics — 82 citations into it; #370 of 1,958 by trust here, #793 by citations
- Computational Mechanics — 62 citations into it; #4,166 of 10,001 by trust here, #6,582 by citations
- Artificial Intelligence — 46 citations into it
- Media Technology — 33 citations into it; #1,129 of 3,621 by trust here, #2,706 by citations
- Ocean Engineering — 28 citations into it; #2,752 of 3,193 by trust here, #3,015 by citations
- Signal Processing — 26 citations into it
- Applied Mathematics — 22 citations into it
Whom Jianwei Ma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- E.J. Candes
- Geoffrey E. Hinton
- Yoshua Bengio
- Felix J. Herrmann
- Alex Krizhevsky
- Yann LeCun
- David L. Donoho
- Kaiming He
- Mauricio D. Sacchi
- Diederik P. Kingma
Who trusts Jianwei Ma overall
- Siwei Yu
- Guochang Liu
- Thomas Wiatowski
- Xiaoqun Zhang
- Zsuzsanna Püspöki
- Jingwei Liang
- Xiaohong Chen
- J. Bredfeldt
- Helmut Bölcskei
- Mauricio D. Sacchi
Papers from Jianwei Ma's perspective
Ordered by how strongly the researchers Jianwei Ma trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Deep Learning for Geophysics: Current and Future Trends (2021) — 383 citations
- Deep-learning inversion: a next generation seismic velocity-model building method (2019) — 513 citations
- Deep learning for denoising (2018) — 308 citations
- Interpolation and denoising of high-dimensional seismic data by learning a tight frame (2015) — 142 citations
- Seismic data restoration via data-driven tight frame (2014) — 97 citations
- Seismic data reconstruction via matrix completion (2013) — 79 citations
- Three-dimensional irregular seismic data reconstruction via low-rank matrix completion (2013) — 165 citations
- The Curvelet Transform (2010) — 450 citations
- Comparisons of wavelets, contourlets and curvelets in seismic denoising (2009) — 127 citations