Shikhar Murty

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 82 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #20,480 of 35,345 by trust here, #26,682 by citations
• Natural Language Processing — 37 citations into it; #4,580 of 5,688 by trust here, #5,593 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #15,979 of 21,575 by trust here, #18,266 by citations
• Evaluation & Benchmarks — 17 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Large Language Models — 7 citations into it; #510 of 799 by trust here, #746 by citations

Whom Shikhar Murty trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Jacob Devlin
5. Jimmy Ba
6. Ido Dagan
7. Alex Krizhevsky
8. Oriol Vinyals
9. Ilya Sutskever
10. J. Urgen Schmidhuber

Who trusts Shikhar Murty overall

1. Joe Stacey
2. Qinyuan Ye
3. Ruiqi Zhong
4. Wangchunshu Zhou
5. Yonatan Belinkov
6. Alec Radford
7. Bill Yuchen Lin
8. Kristy Lee
9. Marek Rei
10. Jinyi Hu

Papers from Shikhar Murty's perspective

Ordered by how strongly the researchers Shikhar Murty 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
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

• ExpBERT: Representation Engineering with Natural Language Explanations (2020) — 45 citations
• DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference (2021) — 30 citations