Matan Atzmon

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 371 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 93 citations into it; #4,584 of 21,575 by trust here, #7,657 by citations
• Computational Mechanics — 83 citations into it; #860 of 10,001 by trust here, #1,031 by citations
• 3D Vision & Pose — 56 citations into it
• Computer Graphics and Computer-Aided Design — 56 citations into it; #282 of 1,584 by trust here, #659 by citations
• Generative Models — 22 citations into it
• Geology — 21 citations into it; #281 of 687 by trust here, #300 by citations
• Aerospace Engineering — 19 citations into it
• Artificial Intelligence — 19 citations into it

Whom Matan Atzmon trusts overall

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

1. Charles Ruizhongtai Qi
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Hao Su
6. Kaiming He
7. Ilya Sutskever
8. Ayan Sinha
9. Yann LeCun
10. Yoshua Bengio

Who trusts Matan Atzmon overall

1. Yue Wang
2. Lior Yariv
3. Yongcheng Liu
4. Vincent Sitzmann
5. Justin M. Solomon
6. Julian Chibane
7. Yinbo Chen
8. Peng Wang
9. Yufeng Zheng
10. Jeremy Reizenstein

Papers from Matan Atzmon's perspective

Ordered by how strongly the researchers Matan Atzmon 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Implicit Geometric Regularization for Learning Shapes (2020) — 1,034 citations
• SAL: Sign Agnostic Learning of Shapes From Raw Data (2019) — 598 citations
• Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance (2020) — 997 citations
• Point convolutional neural networks by extension operators (2018) — 584 citations