Nitin Kamra

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 33 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #5,817 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #4,660 of 21,575 by trust here, #6,954 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Graph Learning — 11 citations into it
• Reinforcement Learning — 10 citations into it; #533 of 2,242 by trust here, #916 by citations
• Generative Models — 8 citations into it; #413 of 1,956 by trust here, #786 by citations
• Meta & Continual Learning — 5 citations into it

Whom Nitin Kamra trusts overall

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

1. R. M. French
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Longxin Lin
6. Yann LeCun
7. Alex Krizhevsky
8. Ilya Sutskever
9. Individualized Treat
10. L. V. D. van der Maaten

Who trusts Nitin Kamra overall

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

Papers from Nitin Kamra's perspective

Ordered by how strongly the researchers Nitin Kamra 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

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
• DynGEM: Deep Embedding Method for Dynamic Graphs (2018) — 423 citations