Anand Rajaraman

Artificial Intelligence · 2006–2015

A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it
• Computer Networks and Communications — 38 citations into it
• Information Systems — 31 citations into it; #1,287 of 8,771 by trust here, #2,912 by citations
• Statistical and Nonlinear Physics — 25 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Management Science and Operations Research — 15 citations into it
• Signal Processing — 15 citations into it
• Information Retrieval & Search — 11 citations into it

Whom Anand Rajaraman trusts overall

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

1. Moses Charikar
2. Monika Henzinger
3. Jianbo Shi
4. Piotr Indyk
5. Siddharth Suri
6. Aristides Gionis
7. Sariel Har-Peled
8. Surajit Chaudhuri
9. Ping Li
10. Christopher R. Palmer

Who trusts Anand Rajaraman overall

1. Ping Li
2. Anshumali Shrivastava
3. Matthew D. Young
4. Natali Ruchansky
5. Yuanzhe Chen
6. Nicola De Cao
7. Spiros V. Georgakopoulos
8. Elvis Saravia
9. Amira Soliman
10. Tong Chen

Papers from Anand Rajaraman's perspective

Ordered by how strongly the researchers Anand Rajaraman 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. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015) — 704 citations
• Mining of Massive Datasets, 2nd Ed (2014) — 394 citations
• Mining of Massive Datasets (2011) — 1,021 citations
• Data integration: the teenage years (2006) — 706 citations