Pedro Rodriguez

Artificial Intelligence · with Boyd-Graber · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 101 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 157 citations into it; #12,682 of 35,345 by trust here, #8,525 by citations
• Natural Language Processing — 100 citations into it; #3,038 of 5,688 by trust here, #3,154 by citations
• Evaluation & Benchmarks — 63 citations into it; #2,821 of 3,147 by trust here, #2,853 by citations
• Computer Vision and Pattern Recognition — 53 citations into it; #18,171 of 21,575 by trust here, #14,478 by citations
• Safety, Robustness & Interpretability — 42 citations into it; #1,256 of 2,251 by trust here, #1,009 by citations
• Information Retrieval & Search — 12 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Pedro Rodriguez trusts overall

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

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

Who trusts Pedro Rodriguez overall

1. Matt Gardner
2. Jordan L. Boyd-Graber
3. Eric Wallace
4. Shi Feng
5. Maharshi Gor
6. Stephanie C. Lin
7. Yoav Artzi
8. W. Zhang
9. Jonathan Berant
10. Adina Williams

Papers from Pedro Rodriguez's perspective

Ordered by how strongly the researchers Pedro Rodriguez 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
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. 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

• Pathologies of Neural Models Make Interpretations Difficult (2018) — 343 citations
• Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering (2018) — 169 citations
• Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? (2021) — 136 citations
• Mitigating Noisy Inputs for Question Answering (2019) — 16 citations
• Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples (2019) — 28 citations
• Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions (2018) — 21 citations