Elie Aljalbout

Computer Vision and Pattern Recognition · 2018

A researcher in the Science corpus: 2 papers; trusts 106 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it
• Artificial Intelligence — 31 citations into it; #31,110 of 35,345 by trust here, #21,878 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• Generative Models — 6 citations into it
• Optimization & Training — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Elie Aljalbout trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Sergey Ioffe
4. Yoshua Bengio
5. Alex Krizhevsky
6. Christian Szegedy
7. Diederik P. Kingma
8. Ross B. Girshick
9. Alec Radford
10. Kaiming He

Who trusts Elie Aljalbout overall

1. Vincent Fortuin
2. Yibo Jiang
3. Matthias Hüser
4. Hadar Serviansky
5. Nakul Verma
6. Francesco Locatello
7. Heiko Strathmann

Papers from Elie Aljalbout's perspective

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

Their papers

• Associative Deep Clustering: Training a Classification Network with No Labels (2018) — 132 citations
• Clustering with Deep Learning: Taxonomy and New Methods (2018) — 265 citations