Umang Gupta

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 41 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #5,558 of 35,345 by trust here, #9,465 by citations
• Computer Vision and Pattern Recognition — 26 citations into it; #4,659 of 21,575 by trust here, #6,954 by citations
• Safety, Robustness & Interpretability — 24 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Safety Research — 20 citations into it
• Generative Models — 10 citations into it; #412 of 1,956 by trust here, #786 by citations
• Reinforcement Learning — 10 citations into it; #532 of 2,242 by trust here, #916 by citations
• Meta & Continual Learning — 5 citations into it

Whom Umang Gupta trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Moritz Hardt
4. Yoshua Bengio
5. Proteek Chandan Roy
6. R. M. French
7. Alex Krizhevsky
8. Yann LeCun
9. Kaiming He
10. Ilya Sutskever

Who trusts Umang Gupta overall

1. Zixuan Ke
2. Gido M. van de Ven
3. Gyuhak Kim
4. Ninareh Mehrabi
5. Bing Liu
6. Honglin Li
7. Pravendra Singh
8. Benedikt Bagus
9. Matthias De Lange
10. Alexander Rainer Tassilo Gepperth

Papers from Umang Gupta's perspective

Ordered by how strongly the researchers Umang 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Generative Dual Memory Network for Continual Learning (2017) — 162 citations
• Deep Fictitious Play for Games with Continuous Action Spaces (2019) — 9 citations
• Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation (2021) — 80 citations
• Attributing Fair Decisions with Attention Interventions (2021) — 23 citations