Charles Lovering

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 48 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 171 citations into it
• Natural Language Processing — 121 citations into it
• Evaluation & Benchmarks — 49 citations into it
• Safety, Robustness & Interpretability — 34 citations into it
• Representation & Self-Supervised Learning — 15 citations into it; #2,395 of 3,145 by trust here, #2,910 by citations
• Model Architectures — 13 citations into it
• Information Systems — 12 citations into it

Whom Charles Lovering trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Elena Voita
6. Richard Socher
7. Jorma Rissanen
8. Samuel R. Bowman
9. Alex Krizhevsky
10. Alex Warstadt

Who trusts Charles Lovering overall

1. Yonatan Belinkov
2. Peter Hase
3. Ethan Perez
4. Mohit Bansal
5. Douwe Kiela

Papers from Charles Lovering's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
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. Gradient-based learning applied to document recognition
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• Predicting Inductive Biases of Pre-Trained Models (2021) — 74 citations