Elad Amrani

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 512 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 95 citations into it; #14,836 of 21,575 by trust here, #11,286 by citations
• Artificial Intelligence — 66 citations into it
• Multimodal Learning — 39 citations into it
• Representation & Self-Supervised Learning — 23 citations into it; #2,351 of 3,145 by trust here, #2,042 by citations
• Signal Processing — 18 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Generative Models — 10 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Elad Amrani trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ross B. Girshick
6. Yoshua Bengio
7. Andrew Zisserman
8. Christian Szegedy
9. Ilya Sutskever
10. Yann LeCun

Who trusts Elad Amrani overall

1. Yasufumi Moriya
2. Antoine Miech
3. Alireza Zareian
4. Ramon Sanabria
5. Alex Wang
6. Florian Metze
7. Linxi (Jim) Fan
8. Brian Chen
9. Yixiao Ge
10. Andrew Rouditchenko

Papers from Elad Amrani's perspective

Ordered by how strongly the researchers Elad Amrani 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning (2020) — 132 citations
• Toward Self-Supervised Object Detection in Unlabeled Videos (2019) — 5 citations
• Learning to Detect and Retrieve Objects From Unlabeled Videos (2019) — 7 citations