Nikhil Kandpal

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 141 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #8,955 of 35,345 by trust here, #3,842 by citations
• Natural Language Processing — 70 citations into it; #2,775 of 5,688 by trust here, #1,316 by citations
• Safety, Robustness & Interpretability — 48 citations into it; #778 of 2,251 by trust here, #313 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Large Language Models — 30 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Nikhil Kandpal trusts overall

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

1. Geoffrey E. Hinton
2. Nicholas Carlini
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alex Graves
7. Alex Krizhevsky
8. Noam Shazeer
9. Yann LeCun
10. Ashish Vaswani

Who trusts Nikhil Kandpal overall

1. Eric Wallace
2. Alex Troy Mallen
3. Nicholas Carlini
4. Daphne Ippolito
5. Stella Biderman
6. Jooyoung Lee
7. Akari Asai
8. Yonatan Oren
9. Emily Sheng
10. Joel Jang

Papers from Nikhil Kandpal's perspective

Ordered by how strongly the researchers Nikhil Kandpal 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Universal Adversarial Triggers for Attacking and Analyzing NLP (2019) — 1,099 citations
• Deduplicating Training Data Mitigates Privacy Risks in Language Models (2022) — 419 citations
• Large Language Models Struggle to Learn Long-Tail Knowledge (2022) — 694 citations
• Universal Adversarial Triggers for NLP (2019) — 30 citations