Shaked Shammah

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 36 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #9,872 of 35,345 by trust here, #8,077 by citations
• Reinforcement Learning — 27 citations into it; #990 of 2,242 by trust here, #1,084 by citations
• Optimization & Training — 19 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Model Architectures — 9 citations into it
• Signal Processing — 6 citations into it

Whom Shaked Shammah trusts overall

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

1. Richard Bellman
2. Richard S. Sutton
3. Shai Shalev-Shwartz
4. Alex Graves
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. J. Urgen Schmidhuber
8. Diederik P. Kingma
9. Sepp Hochreiter
10. Alex Krizhevsky

Who trusts Shaked Shammah overall

1. Edouard Leurent
2. Afshin Oroojlooyjadid
3. David Isele
4. Shai Shalev-Shwartz
5. Mayank Bansal
6. Sen Wang
7. Xiaodan Liang
8. Sorin Mihai Grigorescu
9. Ardi Tampuu
10. Sunae So

Papers from Shaked Shammah's perspective

Ordered by how strongly the researchers Shaked Shammah 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Failures of Gradient-Based Deep Learning (2017) — 231 citations
• Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving (2016) — 940 citations
• On a Formal Model of Safe and Scalable Self-driving Cars (2017) — 851 citations