Meet Shah

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 233 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #9,938 of 21,575 by trust here, #3,955 by citations
• Artificial Intelligence — 80 citations into it; #15,478 of 35,345 by trust here, #5,458 by citations
• Natural Language Processing — 52 citations into it; #3,083 of 5,688 by trust here, #3,977 by citations
• Multimodal Learning — 30 citations into it; #1,129 of 1,211 by trust here, #997 by citations
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 6 citations into it

Whom Meet Shah trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Sepp Hochreiter
4. Yoshua Bengio
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Ross B. Girshick
8. Yann LeCun
9. Ilya Sutskever
10. Shaoqing Ren

Who trusts Meet Shah overall

1. Daniel Khashabi
2. Hao Hao Tan
3. Ben Zion Vatashsky
4. Tushar Khot
5. Ashish Sabharwal
6. Chao Zhang
7. Zhe Gan
8. Amanpreet Singh
9. Ruixue Tang
10. Long Chen

Papers from Meet Shah's perspective

Ordered by how strongly the researchers Meet Shah 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. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Towards VQA Models That Can Read (2019) — 2,180 citations
• Cycle-Consistency for Robust Visual Question Answering (2019) — 215 citations
• MS-Net: Mixed-Supervision Fully-Convolutional Networks for Full-Resolution Segmentation (2018) — 57 citations
• Pythia-A platform for vision & language research (2018)