Nitarshan Rajkumar

Artificial Intelligence · 2022–2023

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 111 citations into it; #21,879 of 35,345 by trust here, #23,077 by citations
• Safety, Robustness & Interpretability — 44 citations into it
• Computer Vision and Pattern Recognition — 34 citations into it
• Natural Language Processing — 15 citations into it; #3,710 of 5,688 by trust here, #4,601 by citations
• Management Science and Operations Research — 12 citations into it

Whom Nitarshan Rajkumar trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Tao Yu
7. Yann LeCun
8. Ilya Sutskever
9. Ruiqi Zhong
10. Torsten Scholak

Who trusts Nitarshan Rajkumar overall

1. M. Pourreza
2. Charles Burton Snell
3. Xinyun Chen
4. Ansong Ni
5. Ruoxi Sun
6. Linlu Qiu
7. Davood Rafiei
8. Zhoujun Cheng
9. Jinyang Li
10. Daniel Klein

Papers from Nitarshan Rajkumar's perspective

Ordered by how strongly the researchers Nitarshan Rajkumar 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Evaluating the Text-to-SQL Capabilities of Large Language Models (2022) — 158 citations
• Harms from Increasingly Agentic Algorithmic Systems (2023) — 259 citations
• Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics (2022) — 36 citations