Prem Gopalan

Artificial Intelligence · 2012–2015

A researcher in the Science corpus: 6 papers; trusts 77 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it
• Statistical and Nonlinear Physics — 47 citations into it; #3,782 of 10,127 by trust here, #7,024 by citations
• Probabilistic & Bayesian Methods — 37 citations into it; #933 of 1,525 by trust here, #1,220 by citations
• Information Systems — 26 citations into it; #2,727 of 8,771 by trust here, #6,195 by citations
• Signal Processing — 22 citations into it
• Statistics and Probability — 21 citations into it
• Recommender Systems — 18 citations into it; #130 of 233 by trust here, #196 by citations
• Management Science and Operations Research — 13 citations into it; #4,050 of 11,946 by trust here, #7,638 by citations

Whom Prem Gopalan trusts overall

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

1. David M. Blei
2. Michael I. Jordan
3. Yehuda Koren
4. John F. Canny
5. Ruslan Salakhutdinov
6. Matthew D. Hoffman
7. Mark Newman
8. Chong Wang
9. Zoubin Ghahramani
10. Donald B. Rubin

Who trusts Prem Gopalan overall

1. David M. Blei
2. Dawen Liang
3. Yin Zheng
4. Susan Athey
5. Jake M. Hofman
6. Maksims Volkovs
7. Ting-Li Chen
8. Francisco J. R. Ruiz
9. Rajesh Ranganath
10. Bangsheng Tang

Papers from Prem Gopalan's perspective

Ordered by how strongly the researchers Prem 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Scalable Recommendation with Poisson Factorization (2013) — 159 citations
• Scalable Recommendation with Hierarchical Poisson Factorization (2015) — 289 citations
• Bayesian Nonparametric Poisson Factorization for Recommendation Systems (2014) — 99 citations
• Content-based recommendations with Poisson factorization (2014) — 194 citations
• Efficient discovery of overlapping communities in massive networks (2013) — 305 citations
• Scalable Inference of Overlapping Communities (2012) — 118 citations