Myriam Tami

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 857 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it; #16,237 of 35,345 by trust here, #18,586 by citations
• Computer Vision and Pattern Recognition — 128 citations into it; #10,340 of 21,575 by trust here, #12,419 by citations
• Representation & Self-Supervised Learning — 86 citations into it; #2,050 of 3,145 by trust here, #2,454 by citations
• Generative Models — 26 citations into it
• Graph Learning — 21 citations into it
• Natural Language Processing — 17 citations into it
• Optimization & Training — 17 citations into it
• Evaluation & Benchmarks — 12 citations into it

Whom Myriam Tami trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Ilya Sutskever
8. Radford M. Neal
9. Ross B. Girshick
10. Karen Simonyan

Who trusts Myriam Tami overall

1. Lihe Yang
2. Chong Zhou
3. Xiangli Yang

Papers from Myriam Tami's perspective

Ordered by how strongly the researchers Myriam Tami 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Semi-Supervised Semantic Segmentation With Cross-Consistency Training (2020) — 964 citations
• An Overview of Deep Semi-Supervised Learning (2020) — 362 citations