Michael Arbel

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 420 researchers and is trusted by 132 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #6,401 of 35,345 by trust here, #6,064 by citations
• Computer Vision and Pattern Recognition — 100 citations into it; #3,000 of 21,575 by trust here, #4,469 by citations
• Generative Models — 73 citations into it; #426 of 1,956 by trust here, #486 by citations
• Probabilistic & Bayesian Methods — 49 citations into it; #1,334 of 1,525 by trust here, #1,244 by citations
• Statistics and Probability — 32 citations into it
• Statistical and Nonlinear Physics — 25 citations into it; #1,066 of 10,127 by trust here, #663 by citations
• Optimization & Training — 16 citations into it
• Signal Processing — 12 citations into it

Whom Michael Arbel trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Aapo Hyvärinen
7. Michael I. Jordan
8. Jianwen Xie
9. Radford M. Neal
10. Kaiming He

Who trusts Michael Arbel overall

1. Abdul Fatir Ansari
2. Ali Borji
3. Yezhen Wang
4. Kwot Sin Lee
5. Gaurav Parmar
6. M. L. Ang
7. Chenlin Meng
8. Tuomas Kynkaanniemi
9. Eric Chan
10. Richard Zhang

Papers from Michael Arbel's perspective

Ordered by how strongly the researchers Michael Arbel 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Demystifying MMD GANs (2018) — 2,101 citations
• Generalized Energy Based Models (2020) — 97 citations
• KALE: When Energy-Based Learning Meets Adversarial Training (2020) — 8 citations