S. Branavan

Artificial Intelligence · 2009–2012

A researcher in the Science corpus: 4 papers; trusts 41 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #12,352 of 35,345 by trust here, #14,181 by citations
• Natural Language Processing — 25 citations into it; #1,061 of 5,688 by trust here, #1,432 by citations
• Reinforcement Learning — 22 citations into it; #563 of 2,242 by trust here, #591 by citations
• Multimodal Learning — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it; #8,552 of 21,575 by trust here, #9,722 by citations

Whom S. Branavan trusts overall

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

1. Richard S. Sutton
2. Raymond J. Mooney
3. Andrew Barto
4. Ronald J. Williams
5. Percy Liang
6. Kobus Barnard
7. Jeffrey Mark Siskind
8. Geoffrey E. Hinton
9. David L. Chen
10. Michael I. Jordan

Who trusts S. Branavan overall

1. Percy Liang
2. Yoav Artzi
3. Jacob Andreas
4. Sahisnu Mazumder
5. Luke S. Zettlemoyer
6. Regina Barzilay
7. Brielen Madureira
8. Daniel Klein
9. Prasoon Goyal
10. Hannaneh Hajishirzi

Papers from S. Branavan's perspective

Ordered by how strongly the researchers S. Branavan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning to Win by Reading Manuals in a Monte-Carlo Framework (2011) — 195 citations
• Learning High-Level Planning from Text (2012) — 75 citations
• Reading between the Lines: Learning to Map High-Level Instructions to Commands (2010) — 134 citations
• Reinforcement Learning for Mapping Instructions to Actions (2009) — 333 citations