Joe Stacey

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 189 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #12,862 of 35,345 by trust here, #27,551 by citations
• Natural Language Processing — 73 citations into it; #2,260 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Safety, Robustness & Interpretability — 19 citations into it; #875 of 2,251 by trust here, #2,041 by citations
• Large Language Models — 15 citations into it
• Model Architectures — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Joe Stacey trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Samuel R. Bowman
5. Ilya Sutskever
6. Sepp Hochreiter
7. Alex Krizhevsky
8. Jacob Devlin
9. Adina Williams
10. Yann LeCun

Who trusts Joe Stacey overall

1. J'er'emy Scheurer
2. Xi Ye
3. Jon Ander Campos
4. Greg Durrett
5. Jun Shern Chan
6. Yonatan Belinkov
7. Yuxiang Wu
8. Angelica Chen
9. Mirac Suzgun
10. Kyunghyun Cho

Papers from Joe Stacey's perspective

Ordered by how strongly the researchers Joe Stacey 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

• Supervising Model Attention with Human Explanations for Robust Natural Language Inference (2021) — 53 citations
• Natural Language Inference with a Human Touch: Using Human Explanations to Guide Model Attention (2021) — 10 citations
• There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training (2020) — 54 citations