Monica Agrawal

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 91 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #13,786 of 35,345 by trust here, #8,999 by citations
• Molecular Biology — 66 citations into it; #5,395 of 17,773 by trust here, #6,817 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Prompting & Reasoning — 34 citations into it
• Natural Language Processing — 33 citations into it
• Computational Theory and Mathematics — 30 citations into it; #5,265 of 12,182 by trust here, #1,851 by citations
• Large Language Models — 27 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Monica Agrawal trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Tomas Mikolov
4. Ilya Sutskever
5. Yoshua Bengio
6. Alex Krizhevsky
7. Yann LeCun
8. Jimmy Ba
9. Kaiming He
10. Alex Graves

Who trusts Monica Agrawal overall

1. Marinka Zitnik
2. William L. Hamilton
3. Rex Ying
4. Michelle M. Li
5. Federico Baldassarre
6. Kexin Huang
7. Jure Leskovec
8. Ming Tu
9. Aravind Sankar
10. Milan Picard

Papers from Monica Agrawal's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
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

• Modeling polypharmacy side effects with graph convolutional networks (2018) — 1,392 citations
• Co-training Improves Prompt-based Learning for Large Language Models (2022) — 47 citations
• Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative (2021) — 94 citations
• Large language models are few-shot clinical information extractors (2022) — 516 citations
• TabLLM: Few-shot Classification of Tabular Data with Large Language Models (2022) — 405 citations
• Large-scale analysis of disease pathways in the human interactome (2017) — 121 citations