Aayush Prakash

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 152 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 93 citations into it; #16,680 of 21,575 by trust here, #8,205 by citations
• Artificial Intelligence — 29 citations into it; #34,017 of 35,345 by trust here, #14,438 by citations
• Evaluation & Benchmarks — 26 citations into it
• Aerospace Engineering — 15 citations into it
• Optimization & Training — 6 citations into it

Whom Aayush Prakash trusts overall

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

1. Kaiming He
2. Shaoqing Ren
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Christian Szegedy
8. Jia Deng
9. Yann LeCun
10. Yoshua Bengio

Who trusts Aayush Prakash overall

1. Sergey I. Nikolenko
2. Jeevan Devaranjan
3. Amlan Kar
4. Jindong Wang
5. Xiangyu Yue
6. Sanja Fidler
7. Timothy M. Hospedales
8. Cuiling Lan
9. Yang Zhang

Papers from Aayush Prakash's perspective

Ordered by how strongly the researchers Aayush Prakash 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. 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

• Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization (2018) — 1,009 citations
• Meta-Sim: Learning to Generate Synthetic Datasets (2019) — 279 citations
• Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data (2018) — 316 citations