Sriraam Natarajan

Artificial Intelligence · 2007–2020

A researcher in the Science corpus: 11 papers; trusts 84 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #5,874 of 35,345 by trust here, #7,728 by citations
• Reinforcement Learning — 38 citations into it
• Probabilistic & Bayesian Methods — 37 citations into it; #656 of 1,525 by trust here, #720 by citations
• Information Systems — 24 citations into it; #6,243 of 8,771 by trust here, #6,770 by citations
• Computational Theory and Mathematics — 13 citations into it
• Computer Networks and Communications — 12 citations into it
• Statistical Learning Theory — 11 citations into it
• Control and Systems Engineering — 10 citations into it

Whom Sriraam Natarajan trusts overall

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

1. Kristian Kersting
2. Jerome H. Friedman
3. Kurt Driessens
4. Hendrik Blockeel
5. Pedro M. Domingos
6. Parag Singla
7. Richard S. Sutton
8. Jennifer Neville
9. Tushar Khot
10. Hoifung Poon

Who trusts Sriraam Natarajan overall

1. Seyed Mehran Kazemi
2. Luc De Raedt
3. Phillip Odom
4. Kristian Kersting
5. Srijita Das
6. Alessandro Lazaric
7. David Poole
8. Nandini Ramanan
9. Mathias Niepert
10. Kendrick Boyd

Papers from Sriraam Natarajan's perspective

Ordered by how strongly the researchers Sriraam Natarajan 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Transfer in variable-reward hierarchical reinforcement learning (2008) — 102 citations
• A Decision-Theoretic Model of Assistance (2007) — 130 citations
• Learning Markov Logic Networks via Functional Gradient Boosting (2011) — 108 citations
• Fitted Q-Learning for Relational Domains (2020) — 17 citations
• Human-Guided Learning for Probabilistic Logic Models (2018) — 20 citations
• Structure learning for relational logistic regression: an ensemble approach (2018) — 12 citations
• Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016) — 327 citations
• Transfer Learning via Relational Type Matching (2015) — 38 citations
• Relational Logistic Regression: The Directed Analog of Markov Logic Networks (2014) — 29 citations
• Gradient-based boosting for statistical relational learning: The relational dependency network case (2011) — 157 citations
• Counting Belief Propagation (2009) — 178 citations