Yaakov Engel

Artificial Intelligence · 2001–2017

A researcher in the Science corpus: 6 papers; trusts 19 researchers and is trusted by 82 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it; #4,098 of 35,345 by trust here, #10,798 by citations
• Reinforcement Learning — 12 citations into it; #302 of 2,242 by trust here, #827 by citations
• Statistical Learning Theory — 8 citations into it
• Optimization & Training — 5 citations into it

Whom Yaakov Engel trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Christopher K. I. Williams
4. Alex Smola
5. John N. Tsitsiklis
6. C. Burges
7. Grace Wahba
8. Jerome H. Friedman
9. Leslie Pack Kaelbling
10. Yuhai Wu

Who trusts Yaakov Engel overall

1. Milica Gasic
2. Sergey Levine
3. Marc Peter Deisenroth
4. Csaba Szepesvari
5. Mohammad Ghavamzadeh
6. Jan Peters
7. Oliver Kroemer
8. Shie Mannor
9. Nikos A. Vlassis
10. Jianfeng Gao

Papers from Yaakov Engel's perspective

Ordered by how strongly the researchers Yaakov Engel 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Reinforcement learning with Gaussian processes (2005) — 430 citations
• Encyclopedia of Machine Learning and Data Mining (2017) — 452 citations
• Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods (2005) — 75 citations
• The kernel recursive least-squares algorithm (2004) — 1,084 citations
• Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning (2003) — 241 citations
• Learning Embedded Maps of Markov Processes (2001) — 19 citations