John P. Lalor

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 4 papers; trusts 47 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it
• Natural Language Processing — 53 citations into it
• Evaluation & Benchmarks — 40 citations into it
• Safety, Robustness & Interpretability — 25 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Management Science and Operations Research — 7 citations into it
• Model Architectures — 6 citations into it

Whom John P. Lalor trusts overall

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

1. Diederik P. Kingma
2. Alex Graves
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Yoon Kim
7. Richard Socher
8. Sepp Hochreiter
9. Tomas Mikolov
10. Alex Krizhevsky

Who trusts John P. Lalor overall

1. Pedro Rodriguez
2. Kawin Ethayarajh

Papers from John P. Lalor's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
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

• Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? (2021) — 136 citations
• Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study (2017) — 41 citations
• Benchmarking Intersectional Biases in NLP (2022) — 56 citations
• Citation Analysis with Neural Attention Models (2016) — 22 citations