Peter Hase

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 7 papers; trusts 472 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 257 citations into it; #5,526 of 35,345 by trust here, #9,465 by citations
• Natural Language Processing — 110 citations into it; #1,818 of 5,688 by trust here, #3,369 by citations
• Computer Vision and Pattern Recognition — 77 citations into it
• Safety, Robustness & Interpretability — 72 citations into it; #714 of 2,251 by trust here, #1,329 by citations
• Large Language Models — 58 citations into it
• Reinforcement Learning — 44 citations into it
• Evaluation & Benchmarks — 36 citations into it; #3,017 of 3,147 by trust here, #2,704 by citations
• Prompting & Reasoning — 23 citations into it

Whom Peter Hase trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ilya Sutskever
6. Kaiming He
7. Yann LeCun
8. Karen Simonyan
9. Jimmy Ba
10. Ruslan Salakhutdinov

Who trusts Peter Hase overall

1. J'er'emy Scheurer
2. Swarnadeep Saha
3. Joe Stacey
4. Mohit Bansal
5. Jon Ander Campos
6. Angelica Chen
7. Alon Jacovi
8. Tongshuang Sherry Wu
9. Yonatan Belinkov
10. Andrew Kyle Lampinen

Papers from Peter Hase's perspective

Ordered by how strongly the researchers Peter Hase 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data (2021) — 95 citations
• Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs (2021) — 92 citations
• Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? (2020) — 120 citations
• Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? (2020) — 374 citations
• Interpretable Image Recognition with Hierarchical Prototypes (2019) — 138 citations
• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023) — 934 citations
• Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations (2022) — 18 citations