Abhishek Chaurasia

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 539 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 53 citations into it; #5,377 of 21,575 by trust here, #5,422 by citations
• Artificial Intelligence — 31 citations into it
• Model Architectures — 19 citations into it
• Optimization & Training — 9 citations into it
• Aerospace Engineering — 7 citations into it

Whom Abhishek Chaurasia trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Yann LeCun
6. Karen Simonyan
7. Yoshua Bengio
8. Ilya Sutskever
9. Ross B. Girshick
10. Sergey Ioffe

Who trusts Abhishek Chaurasia overall

1. Alexey A. Shvets
2. Lichen Zhou
3. Zongwei Zhou
4. Marin Orsic
5. Gaston Lenczner
6. Changqian Yu
7. Favyen Bastani
8. Alexander Rakhlin
9. Vladimir I. Iglovikov
10. Eduardo Romera

Papers from Abhishek Chaurasia's perspective

Ordered by how strongly the researchers Abhishek Chaurasia trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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

• ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation (2016) — 2,375 citations
• LinkNet: Exploiting encoder representations for efficient semantic segmentation (2017) — 1,712 citations