Amal Rannen Triki

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 3 papers; trusts 407 researchers and is trusted by 103 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #3,195 of 21,575 by trust here, #4,174 by citations
• Artificial Intelligence — 31 citations into it; #6,094 of 35,345 by trust here, #6,569 by citations
• Evaluation & Benchmarks — 12 citations into it
• Optimization & Training — 11 citations into it; #552 of 2,601 by trust here, #1,195 by citations
• Representation & Self-Supervised Learning — 8 citations into it; #961 of 3,145 by trust here, #1,062 by citations
• Model Architectures — 5 citations into it

Whom Amal Rannen Triki trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Andrew Zisserman
5. Ross B. Girshick
6. Karen Simonyan
7. L. Eon Bottou
8. Diederik P. Kingma
9. Matthew D. Zeiler
10. Ilya Sutskever

Who trusts Amal Rannen Triki overall

1. Rahaf Aljundi
2. Arun Mallya
3. Zheda Mai
4. Matthias De Lange
5. Tinne Tuytelaars
6. Marc Masana
7. Pedro Morgado
8. Mohamed Elhoseiny
9. Zhen Wang
10. Marcus Rohrbach

Papers from Amal Rannen Triki's perspective

Ordered by how strongly the researchers Amal Rannen Triki 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Encoder Based Lifelong Learning (2017) — 348 citations
• The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks (2017) — 997 citations
• The Lov´asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks