Piyawat Lertvittayakumjorn

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 229 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #26,352 of 35,345 by trust here, #20,377 by citations
• Natural Language Processing — 76 citations into it; #4,326 of 5,688 by trust here, #4,183 by citations
• Safety, Robustness & Interpretability — 59 citations into it; #1,723 of 2,251 by trust here, #2,041 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Large Language Models — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Information Systems — 7 citations into it

Whom Piyawat Lertvittayakumjorn trusts overall

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

1. Marco Tulio Ribeiro
2. Geoffrey E. Hinton
3. Leila Arras
4. Yann LeCun
5. Yoshua Bengio
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Ilya Sutskever
9. Richard Socher
10. Jeffrey Pennington

Who trusts Piyawat Lertvittayakumjorn overall

1. Andrew Kyle Lampinen
2. Huihan Yao
3. Francesca Toni
4. Lucia Specia
5. Sina Mohseni
6. Ishita Dasgupta
7. Ying Chen
8. Stephanie C. Y. Chan

Papers from Piyawat Lertvittayakumjorn's perspective

Ordered by how strongly the researchers Piyawat Lertvittayakumjorn 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Explanation-Based Human Debugging of NLP Models: A Survey (2021) — 90 citations
• Human-grounded Evaluations of Explanation Methods for Text Classification (2019) — 73 citations
• Rational LAMOL: A Rationale-based Lifelong Learning Framework (2021) — 23 citations
• Human-in-the-loop Debugging Deep Text Classifiers (2020) — 57 citations