Nir Ailon

Computer Vision and Pattern Recognition · 2002–2016

A researcher in the Science corpus: 7 papers; trusts 263 researchers and is trusted by 1,145 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 44 citations into it; #1,261 of 21,575 by trust here, #3,006 by citations
• Artificial Intelligence — 37 citations into it; #1,812 of 35,345 by trust here, #5,252 by citations
• Representation & Self-Supervised Learning — 22 citations into it; #268 of 3,145 by trust here, #825 by citations
• Model Architectures — 9 citations into it
• Generative Models — 7 citations into it
• Signal Processing — 7 citations into it
• Computer Networks and Communications — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Nir Ailon trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Yisong Yue
4. Yoshua Bengio
5. Alex Krizhevsky
6. Thorsten Joachims
7. Piotr Indyk
8. Tamás Sarlós
9. Svante Janson
10. Matthew D. Zeiler

Who trusts Nir Ailon overall

1. Petros Drineas
2. David P. Woodruff
3. Tamás Sarlós
4. Joel A. Tropp
5. Felix X. Yu
6. Krzysztof Marcin Choromanski
7. Sariel Har-Peled
8. Per-Gunnar Martinsson
9. Aadirupa Saha
10. Jan Vyb'iral

Papers from Nir Ailon's perspective

Ordered by how strongly the researchers Nir Ailon 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Deep Metric Learning Using Triplet Network (2014) — 2,210 citations
• Deep unsupervised learning through spatial contrasting (2016) — 23 citations
• Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform (2006) — 554 citations
• The Fast Johnson--Lindenstrauss Transform and Approximate Nearest Neighbors (2009) — 582 citations
• Reducing Dueling Bandits to Cardinal Bandits (2014) — 157 citations
• Aggregating inconsistent information: ranking and clustering (2005) — 317 citations
• Torsion points on curves and common divisors of $a^k-1$ and $b^k-1$ (2002) — 54 citations