Tamar Rott Shaham

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 1 paper; trusts 289 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 50 citations into it; #21,227 of 21,575 by trust here, #8,831 by citations
• Generative Models — 30 citations into it; #1,947 of 1,956 by trust here, #1,010 by citations
• Computer Graphics and Computer-Aided Design — 11 citations into it
• Artificial Intelligence — 9 citations into it
• Media Technology — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Tamar Rott Shaham trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Jun-Yan Zhu
5. Alex Krizhevsky
6. Yochai Blau
7. Christian Szegedy
8. Yann LeCun
9. Yoshua Bengio
10. Phillip Isola

Who trusts Tamar Rott Shaham overall

1. Taesung Park
2. Yingxue Pang
3. Chenshuang Zhang
4. Chieh Hubert Lin
5. Elizaveta Logacheva
6. Rui Xu
7. Martin G. Schultz
8. Jianrong Zhang
9. Xintao Wang
10. Li Luo

Papers from Tamar Rott Shaham's perspective

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

Their papers

• SinGAN: Learning a Generative Model From a Single Natural Image (2019) — 954 citations