Tejas Khot

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 405 researchers and is trusted by 733 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 116 citations into it; #1,588 of 21,575 by trust here, #1,329 by citations
• Artificial Intelligence — 65 citations into it; #2,110 of 35,345 by trust here, #1,878 by citations
• Multimodal Learning — 38 citations into it; #109 of 1,211 by trust here, #114 by citations
• Computational Mechanics — 35 citations into it
• Natural Language Processing — 21 citations into it
• Aerospace Engineering — 20 citations into it
• Computer Graphics and Computer-Aided Design — 16 citations into it
• Geology — 16 citations into it

Whom Tejas Khot 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. Karen Simonyan
7. Yoshua Bengio
8. Yann LeCun
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Tejas Khot overall

1. Ronghang Hu
2. Jaemin Cho
3. Zhe Gan
4. Drew A. Hudson
5. Mohit Bansal
6. Zhou Yu
7. Linjie Li
8. Liunian Harold Li
9. Jianfeng Wang
10. Jiasen Lu

Papers from Tejas Khot's perspective

Ordered by how strongly the researchers Tejas Khot 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. Long Short-Term Memory
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

• Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering (2016) — 4,360 citations
• PCN: Point Completion Network (2018) — 1,083 citations