Stephanie C. Y. Chan

Artificial Intelligence · 2011–2022

A researcher in the Science corpus: 10 papers; trusts 134 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 289 citations into it; #34,891 of 35,345 by trust here, #7,112 by citations
• Computer Vision and Pattern Recognition — 90 citations into it
• Natural Language Processing — 89 citations into it
• Reinforcement Learning — 78 citations into it
• Prompting & Reasoning — 41 citations into it
• Cognitive Science & Psychology — 32 citations into it
• Model Architectures — 32 citations into it
• Management Science and Operations Research — 28 citations into it

Whom Stephanie C. Y. Chan trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Sepp Hochreiter
4. Diederik P. Kingma
5. Richard S. Sutton
6. Yoshua Bengio
7. J. Urgen Schmidhuber
8. Alex Krizhevsky
9. Ilya Sutskever
10. Yann LeCun

Who trusts Stephanie C. Y. Chan overall

1. Georgios Papoudakis
2. Jing Yu Koh
3. Jason Wei
4. J'er'emy Scheurer
5. Jie Huang
6. Filippos Christianos

Papers from Stephanie C. Y. Chan's perspective

Ordered by how strongly the researchers Stephanie C. Y. Chan 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Can language models learn from explanations in context? (2022) — 383 citations
• Semantic Exploration from Language Abstractions and Pretrained Representations (2022) — 84 citations
• Language models show human-like content effects on reasoning (2022) — 230 citations
• Measuring the Reliability of Reinforcement Learning Algorithms (2019) — 101 citations
• Data Distributional Properties Drive Emergent In-Context Learning in Transformers (2022) — 389 citations
• Towards mental time travel: a hierarchical memory for reinforcement learning agents (2021) — 61 citations
• Tell me why! Explanations support learning relational and causal structure (2021) — 51 citations
• Tell me why! - Explanations support learning of relational and causal structure (2021) — 6 citations
• On the value of information and other rewards (2011) — 23 citations
• Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers