Aviv Eisenschtat

Computer Vision and Pattern Recognition · 2016

A researcher in the Science corpus: 1 paper; trusts 203 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 28 citations into it; #8,978 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 22 citations into it
• Multimodal Learning — 11 citations into it; #482 of 1,211 by trust here, #962 by citations
• Representation & Self-Supervised Learning — 9 citations into it; #1,520 of 3,145 by trust here, #2,404 by citations
• Model Architectures — 5 citations into it

Whom Aviv Eisenschtat trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Pascal Vincent
5. Karen Simonyan
6. Alex Krizhevsky
7. Sergey Ioffe
8. Hugo Larochelle
9. Kaiming He
10. Marc'Aurelio Ranzato

Who trusts Aviv Eisenschtat overall

1. Fartash Faghri
2. Ryan Eloff
3. Liwei Wang
4. Hao Wu
5. Nicola Messina
6. David J. Fleet
7. Herman Arnold Engelbrecht
8. Yin Li
9. Jamie Ryan Kiros
10. Giuseppe Amato

Papers from Aviv Eisenschtat's perspective

Ordered by how strongly the researchers Aviv Eisenschtat 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Linking Image and Text with 2-Way Nets (2016) — 184 citations