Justin M. Solomon

Computer Vision and Pattern Recognition · 2012–2021

A researcher in the Science corpus: 9 papers; trusts 783 researchers and is trusted by 123 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 189 citations into it; #3,101 of 21,575 by trust here, #2,410 by citations
• Computational Mechanics — 119 citations into it; #662 of 10,001 by trust here, #266 by citations
• Artificial Intelligence — 78 citations into it
• Aerospace Engineering — 59 citations into it; #2,883 of 6,421 by trust here, #1,890 by citations
• Geology — 49 citations into it; #482 of 687 by trust here, #534 by citations
• Computer Graphics and Computer-Aided Design — 38 citations into it; #1,176 of 1,584 by trust here, #1,205 by citations
• Graph Learning — 34 citations into it; #369 of 693 by trust here, #106 by citations
• Model Architectures — 34 citations into it

Whom Justin M. Solomon trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Kaiming He
4. Alex Krizhevsky
5. Yoshua Bengio
6. Ross B. Girshick
7. Yann LeCun
8. Jimmy Ba
9. Ilya Sutskever
10. Radford M. Neal

Who trusts Justin M. Solomon overall

1. Yue Wang
2. Christopher Bongsoo Choy
3. Shengyu Huang
4. Zi Jian Yew
5. Adrien Poulenard
6. Zaiwei Zhang
7. Saining Xie
8. Juyong Zhang
9. Wei Dong
10. M. Ovsjanikov

Papers from Justin M. Solomon's perspective

Ordered by how strongly the researchers Justin M. Solomon 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Dynamic Graph CNN for Learning on Point Clouds (2018) — 7,693 citations
• DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries (2021) — 1,126 citations
• Deep Closest Point: Learning Representations for Point Cloud Registration (2019) — 1,065 citations
• Functional maps (2012) — 440 citations
• Functional Maps Representation On Product Manifolds (2018) — 19 citations
• PRNet: Self-Supervised Learning for Partial-to-Partial Registration (2019) — 466 citations
• GWCNN: A Metric Alignment Layer for Deep Shape Analysis (2017) — 105 citations
• Gromov-Wasserstein Averaging of Kernel and Distance Matrices (2016) — 505 citations
• Exponential Integration for Hamiltonian Monte Carlo (2015) — 21 citations