Gunshi Gupta

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 66 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 29 citations into it; #13,704 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 24 citations into it; #22,103 of 35,345 by trust here, #22,463 by citations
• Aerospace Engineering — 11 citations into it
• Optimization & Training — 11 citations into it
• Meta & Continual Learning — 10 citations into it; #705 of 1,041 by trust here, #808 by citations
• Reinforcement Learning — 10 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Gunshi Gupta trusts overall

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

1. Marc'Aurelio Ranzato
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. N. Schraudolph
5. Arslan Chaudhry
6. Alex Krizhevsky
7. J. Urgen Schmidhuber
8. Yoshua Bengio
9. Yann LeCun
10. Kaiming He

Who trusts Gunshi Gupta overall

1. Jorge Armando Mendez Mendez
2. Krishnan Raghavan
3. Glen Berseth
4. Danruo Deng
5. Haeyong Kang
6. Christian Alexander Steinparz
7. Eric Eaton
8. Prasanna Balaprakash
9. Zhiwei Zhang
10. Guangyong Chen

Papers from Gunshi Gupta's perspective

Ordered by how strongly the researchers Gunshi Gupta 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. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• La-MAML: Look-ahead Meta Learning for Continual Learning (2020) — 81 citations
• Geometric Consistency for Self-Supervised End-to-End Visual Odometry (2018) — 53 citations