Rajat Raina

Computer Vision and Pattern Recognition · 2003–2009

A researcher in the Science corpus: 5 papers; trusts 65 researchers and is trusted by 204 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 20 citations into it
• Representation & Self-Supervised Learning — 19 citations into it; #310 of 3,145 by trust here, #249 by citations
• Artificial Intelligence — 18 citations into it; #1,919 of 35,345 by trust here, #2,810 by citations
• Natural Language Processing — 12 citations into it
• Optimization & Training — 11 citations into it; #972 of 2,601 by trust here, #817 by citations
• Cognitive Neuroscience — 6 citations into it; #594 of 10,852 by trust here, #803 by citations
• Generative Models — 5 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Rajat Raina trusts overall

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

1. Andrew Y. Ng
2. Honglak Lee
3. Geoffrey E. Hinton
4. Ken Lang
5. Barbara Olshausen
6. Sebastian Thrun
7. R. Caruana
8. Yoshua Bengio
9. Jonathan Baxter
10. David D. Lewis

Who trusts Rajat Raina overall

1. Yoshua Bengio
2. Quoc V. Le
3. Honglak Lee
4. J. Urgen Schmidhuber
5. Julien Mairal
6. Qiang Yang
7. Trevor Darrell
8. Marc'Aurelio Ranzato
9. Sergey Levine
10. Kyunghyun Cho

Papers from Rajat Raina's perspective

Ordered by how strongly the researchers Rajat Raina 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Self-taught learning: transfer learning from unlabeled data (2007) — 1,809 citations
• Large-scale deep unsupervised learning using graphics processors (2009) — 784 citations
• Constructing informative priors using transfer learning (2006) — 331 citations
• Efficient sparse coding algorithms (2006) — 2,878 citations
• Classification with Hybrid Generative/Discriminative Models (2003) — 254 citations