Abhronil Sengupta

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 3 papers; trusts 155 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 38 citations into it; #19,763 of 21,575 by trust here, #16,787 by citations
• Artificial Intelligence — 30 citations into it; #32,157 of 35,345 by trust here, #25,809 by citations
• Model Architectures — 15 citations into it; #2,022 of 2,278 by trust here, #2,037 by citations
• Electrical and Electronic Engineering — 9 citations into it; #8,595 of 16,539 by trust here, #9,402 by citations
• Optimization & Training — 9 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Efficiency & Compression — 6 citations into it; #1,134 of 1,264 by trust here, #1,130 by citations
• Cognitive Neuroscience — 5 citations into it; #6,242 of 10,852 by trust here, #5,990 by citations

Whom Abhronil Sengupta trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Karen Simonyan
7. Ilya Sutskever
8. Christian Szegedy
9. X. Zhang
10. Shaoqing Ren

Who trusts Abhronil Sengupta overall

1. Wei Fang
2. Nitin Rathi
3. Kaushik Roy
4. Priyadarshini Panda
5. Gopalakrishnan Srinivasan
6. Bing Han
7. Zhaofei Yu
8. Shi-Wee Deng
9. Kashu Yamazaki
10. Seijoon Kim

Papers from Abhronil Sengupta's perspective

Ordered by how strongly the researchers Abhronil Sengupta 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Going Deeper in Spiking Neural Networks: VGG and Residual Architectures (2018) — 1,229 citations
• Energy-Efficient and Improved Image Recognition with Conditional Deep Learning (2017) — 43 citations
• Conditional Deep Learning for energy-efficient and enhanced pattern recognition (2015) — 208 citations