Gilad Yehudai

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #14,178 of 35,345 by trust here, #15,866 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Optimization & Training — 24 citations into it
• Statistical Learning Theory — 20 citations into it; #625 of 1,038 by trust here, #662 by citations
• Scaling & Learning Theory — 17 citations into it
• Efficiency & Compression — 13 citations into it
• Model Architectures — 10 citations into it
• Computational Mechanics — 7 citations into it

Whom Gilad Yehudai trusts overall

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

1. Ali Rahimi
2. Zeyuan Allen-Zhu
3. Yuanzhi Li
4. Shai Shalev-Shwartz
5. B. Recht
6. Simon Shaolei Du
7. Amit Daniely
8. Yoshua Bengio
9. Xavier Glorot
10. Geoffrey E. Hinton

Who trusts Gilad Yehudai overall

1. Yuan Cao
2. Jonathan Frankle
3. Behrooz Ghorbani
4. Quanquan Gu
5. Yuanzhi Li
6. Zeyuan Allen-Zhu
7. David J. Schwab
8. Jason D. Lee
9. Eran Malach
10. Yu Bai

Papers from Gilad Yehudai's perspective

Ordered by how strongly the researchers Gilad Yehudai 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. Learning Multiple Layers of Features from Tiny Images
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Going deeper with convolutions

Their papers

• Proving the Lottery Ticket Hypothesis: Pruning is All You Need (2020) — 335 citations
• On the Power and Limitations of Random Features for Understanding Neural Networks (2019) — 196 citations