Sawan Kumar

Artificial Intelligence · with Talukdar · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 86 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #6,339 of 35,345 by trust here, #15,542 by citations
• Natural Language Processing — 25 citations into it; #1,465 of 5,688 by trust here, #3,638 by citations
• Large Language Models — 20 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Safety, Robustness & Interpretability — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Sawan Kumar trusts overall

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

1. Alex Wang
2. Geoffrey E. Hinton
3. Alec Radford
4. Yoshua Bengio
5. Richard Socher
6. Diederik P. Kingma
7. Samuel R. Bowman
8. Ilya Sutskever
9. Jacob Devlin
10. Alex Krizhevsky

Who trusts Sawan Kumar overall

1. Joe Stacey
2. Bodhisattwa Prasad Majumder
3. Timo Schick
4. Ana Marasovic
5. Xinyan Zhao
6. Sarah Wiegreffe
7. Oana-Maria Camburu
8. Peter Hase
9. Yonatan Belinkov
10. Andreas Madsen

Papers from Sawan Kumar's perspective

Ordered by how strongly the researchers Sawan Kumar 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Reordering Examples Helps during Priming-based Few-Shot Learning (2021) — 71 citations
• NILE : Natural Language Inference with Faithful Natural Language Explanations (2020) — 174 citations