Haggai Maron

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 9 papers; trusts 585 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 227 citations into it; #12,375 of 21,575 by trust here, #8,296 by citations
• Artificial Intelligence — 154 citations into it; #4,652 of 35,345 by trust here, #7,346 by citations
• Computational Mechanics — 79 citations into it; #2,672 of 10,001 by trust here, #1,556 by citations
• Model Architectures — 79 citations into it; #2,167 of 2,278 by trust here, #1,572 by citations
• Graph Learning — 78 citations into it; #241 of 693 by trust here, #339 by citations
• Generative Models — 63 citations into it
• Computer Graphics and Computer-Aided Design — 40 citations into it
• Representation & Self-Supervised Learning — 33 citations into it

Whom Haggai Maron trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Charles Ruizhongtai Qi
4. Diederik P. Kingma
5. Yann LeCun
6. Ilya Sutskever
7. Max Welling
8. Yoshua Bengio
9. Kaiming He
10. Taco Cohen

Who trusts Haggai Maron overall

1. Vijay Prakash Dwivedi
2. Zhengdao Chen
3. Taco Cohen
4. Jiaxuan You
5. Hadar Serviansky
6. Nimrod Segol
7. Christopher Morris
8. Yaron Lipman
9. William L. Hamilton
10. Johannes Klicpera

Papers from Haggai Maron's perspective

Ordered by how strongly the researchers Haggai Maron 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Convolutional neural networks on surfaces via seamless toric covers (2017) — 246 citations
• StyleGAN-NADA (2021) — 319 citations
• On Learning Sets of Symmetric Elements (2020) — 152 citations
• Invariant and Equivariant Graph Networks (2018) — 576 citations
• On the Universality of Invariant Networks (2019) — 275 citations
• Provably Powerful Graph Networks (2019) — 683 citations
• Multi-chart generative surface modeling (2018) — 81 citations
• Set2Graph: Learning Graphs From Sets (2020) — 37 citations
• Point convolutional neural networks by extension operators (2018) — 584 citations