Moran Baruch

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 15 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 20 citations into it; #20,011 of 35,345 by trust here, #18,186 by citations
• Optimization & Training — 10 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Federated & Distributed Learning — 7 citations into it; #174 of 319 by trust here, #237 by citations
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Moran Baruch trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Mu Li
4. John C. Duchi
5. Sergey Zagoruyko
6. Geoffrey E. Hinton
7. El Mahdi El Mhamdi
8. Cong Xie
9. Shiqi Shen
10. Dong Yin

Who trusts Moran Baruch overall

1. Virat Shejwalkar
2. Chien-Lun Chen
3. Shashank Rajput
4. Zaixi Zhang
5. Hong Chang
6. Amir Houmansadr
7. Hongyi Wang
8. T. D. Nguyen
9. Lie He
10. Leana Golubchik

Papers from Moran Baruch's perspective

Ordered by how strongly the researchers Moran Baruch 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. Gradient-based learning applied to document recognition
4. Learning Multiple Layers of Features from Tiny Images
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Little Is Enough: Circumventing Defenses For Distributed Learning (2019) — 732 citations