Anoop Sarkar

Artificial Intelligence · 2003–2019

A researcher in the Science corpus: 9 papers; trusts 117 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #10,947 of 35,345 by trust here, #11,192 by citations
• Natural Language Processing — 59 citations into it; #1,509 of 5,688 by trust here, #2,605 by citations
• Optimization & Training — 25 citations into it; #1,848 of 2,601 by trust here, #1,986 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Computational Mechanics — 9 citations into it
• Model Architectures — 9 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Anoop Sarkar trusts overall

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

1. Franz Joseph Och
2. David Yarowsky
3. Michael Collins
4. Peter F. Brown
5. Andreas Stolcke
6. Steven P. Abney
7. Thorsten Brants
8. Ilya Sutskever
9. Yoshua Bengio
10. Geoffrey E. Hinton

Who trusts Anoop Sarkar overall

1. Liang Huang
2. Ronan Collobert
3. Holger Schwenk
4. Percy Liang
5. Anton Bakhtin
6. Kevin Gimpel
7. Yuntian Deng
8. Yixin Liu
9. Michael Collins
10. Jason Aaron Edward Weston

Papers from Anoop Sarkar's perspective

Ordered by how strongly the researchers Anoop Sarkar 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Voting Between Multiple Data Representations for Text Chunking (2005) — 77 citations
• Interrogating the Explanatory Power of Attention in Neural Machine Translation (2019) — 17 citations
• Semi-supervised model adaptation for statistical machine translation (2007) — 60 citations
• Example Selection for Bootstrapping Statistical Parsers (2003) — 290 citations
• Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields (2015) — 83 citations
• Transductive learning for statistical machine translation (2007) — 131 citations
• A Clustering Approach for Nearly Unsupervised Recognition of Nonliteral Language (2006) — 294 citations
• Discriminative Reranking for Machine Translation (2004) — 256 citations
• A Smorgasbord of Features for Statistical Machine Translation (2004) — 325 citations