Raghuraman Gopalan

Artificial Intelligence · 2011–2015

A researcher in the Science corpus: 5 papers; trusts 328 researchers and is trusted by 1,210 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #1,777 of 35,345 by trust here, #3,167 by citations
• Computer Vision and Pattern Recognition — 88 citations into it; #1,206 of 21,575 by trust here, #2,360 by citations
• Representation & Self-Supervised Learning — 69 citations into it; #236 of 3,145 by trust here, #395 by citations
• Computational Mechanics — 13 citations into it
• Signal Processing — 13 citations into it
• Cancer Research — 10 citations into it
• Media Technology — 9 citations into it; #3,047 of 3,621 by trust here, #3,318 by citations
• Statistical Learning Theory — 8 citations into it

Whom Raghuraman Gopalan trusts overall

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

1. Lotfi A. Zadeh
2. Geoffrey E. Hinton
3. John Blitzer
4. Yoshua Bengio
5. Peter N. Belhumeur
6. Yann LeCun
7. Alex Krizhevsky
8. Hal Daumé Iii
9. Pierre-Antoine Absil
10. Kaiming He

Who trusts Raghuraman Gopalan overall

1. Gabriella Csurka
2. Judy Hoffman
3. Wen Li
4. Baochen Sun
5. Kate Saenko
6. Boqing Gong
7. Vishal M. Patel
8. Ramalingam Chellappa
9. Mohammad Rostami
10. Hossein Rahmani

Papers from Raghuraman Gopalan's perspective

Ordered by how strongly the researchers Raghuraman Gopalan 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. 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Domain adaptation for object recognition: An unsupervised approach (2011) — 1,114 citations
• Visual Domain Adaptation: A survey of recent advances (2015) — 884 citations
• Unsupervised Adaptation Across Domain Shifts by Generating Intermediate Data Representations (2014) — 213 citations
• A Blur-Robust Descriptor with Applications to Face Recognition (2012) — 98 citations
• DLID: Deep Learning for Domain Adaptation by Interpolating between Domains (2013) — 207 citations