Ishmael Belghazi

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 73 researchers and is trusted by 484 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 52 citations into it; #1,933 of 21,575 by trust here, #2,191 by citations
• Generative Models — 41 citations into it; #543 of 1,956 by trust here, #256 by citations
• Artificial Intelligence — 39 citations into it; #6,854 of 35,345 by trust here, #2,908 by citations
• Statistical and Nonlinear Physics — 11 citations into it; #1,686 of 10,127 by trust here, #194 by citations
• Probabilistic & Bayesian Methods — 10 citations into it; #593 of 1,525 by trust here, #110 by citations
• Signal Processing — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it; #2,181 of 3,145 by trust here, #1,543 by citations
• Information Theory — 5 citations into it

Whom Ishmael Belghazi trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yann LeCun
6. Ilya Sutskever
7. Tim Salimans
8. Kaiming He
9. Ian J. Goodfellow
10. Alex Graves

Who trusts Ishmael Belghazi overall

1. Jiaming Song
2. Yoshua Bengio
3. Fei Ye
4. Andrea Vedaldi
5. Stefano Ermon
6. Sergey Levine
7. Chunyuan Li
8. Richard Zhang
9. Jun-Yan Zhu
10. Chen Sun

Papers from Ishmael Belghazi's perspective

Ordered by how strongly the researchers Ishmael Belghazi 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Adversarially Learned Inference (2016) — 1,346 citations
• MINE: Mutual Information Neural Estimation (2018) — 747 citations