Behrooz Ghorbani

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 8 papers; trusts 63 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 163 citations into it; #12,077 of 35,345 by trust here, #8,283 by citations
• Optimization & Training — 80 citations into it; #1,795 of 2,601 by trust here, #1,349 by citations
• Computer Vision and Pattern Recognition — 57 citations into it; #13,336 of 21,575 by trust here, #11,721 by citations
• Scaling & Learning Theory — 56 citations into it
• Model Architectures — 41 citations into it; #1,277 of 2,278 by trust here, #1,300 by citations
• Statistical Learning Theory — 38 citations into it; #669 of 1,038 by trust here, #443 by citations
• Statistical and Nonlinear Physics — 27 citations into it; #4,924 of 10,127 by trust here, #2,578 by citations
• Computational Mechanics — 26 citations into it

Whom Behrooz Ghorbani trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Diederik P. Kingma
4. Francis R. Bach
5. Yoshua Bengio
6. Alex Krizhevsky
7. Sergey Ioffe
8. Yann LeCun
9. Christian Szegedy
10. Kaiming He

Who trusts Behrooz Ghorbani overall

1. Namuk Park
2. Alberto Bietti
3. Jiaoyang Huang
4. Zi-Mei Yao
5. Shuangfei Zhai
6. Stanislav Fort
7. Song Mei
8. Greg Yang
9. Pierre Foret
10. Andrea Montanari

Papers from Behrooz Ghorbani's perspective

Ordered by how strongly the researchers Behrooz Ghorbani 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Scaling Laws for Neural Machine Translation (2021) — 136 citations
• An Investigation into Neural Net Optimization via Hessian Eigenvalue Density (2019) — 460 citations
• Linearized two-layers neural networks in high dimension (2019) — 274 citations
• Adaptive Gradient Methods at the Edge of Stability (2022) — 86 citations
• Limitations of Lazy Training of Two-layers Neural Networks (2019) — 152 citations
• Discussion of: “Nonparametric regression using deep neural networks with ReLU activation function” (2020) — 27 citations
• GPT-4o System Card (2024) — 4,622 citations
• When do neural networks outperform kernel methods? (2020) — 214 citations