Ehud Ben-Reuven

Artificial Intelligence · 2016

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

Subfields

• Artificial Intelligence — 18 citations into it; #25,397 of 35,345 by trust here, #34,943 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Signal Processing — 6 citations into it; #5,300 of 7,572 by trust here, #7,486 by citations
• Optimization & Training — 5 citations into it

Whom Ehud Ben-Reuven trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Alex Krizhevsky
5. Harri Valpola
6. Sergey Ioffe
7. Diederik P. Kingma
8. Christian Szegedy
9. Yves Grandvalet
10. Nitish Srivastava

Who trusts Ehud Ben-Reuven overall

1. Lu Jiang
2. A. Navon
3. Curtis G. Northcutt
4. Bo Han
5. Yueming Lyu
6. Zhilu Zhang
7. Eran Malach
8. Yosi Keller
9. Hwanjun Song
10. Junnan Li

Papers from Ehud Ben-Reuven's perspective

Ordered by how strongly the researchers Ehud Ben-Reuven 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. ImageNet: A large-scale hierarchical image database
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 Semisupervised Approach for Language Identification based on Ladder Networks (2016) — 5 citations
• Training deep neural-networks using a noise adaptation layer (2016) — 831 citations