Jane X. Wang

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 13 papers; trusts 156 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 352 citations into it; #9,098 of 35,345 by trust here, #2,637 by citations
• Reinforcement Learning — 168 citations into it; #881 of 2,242 by trust here, #306 by citations
• Computer Vision and Pattern Recognition — 116 citations into it
• Cognitive Neuroscience — 81 citations into it; #2,823 of 10,852 by trust here, #611 by citations
• Natural Language Processing — 65 citations into it
• Representation & Self-Supervised Learning — 47 citations into it
• Model Architectures — 40 citations into it
• Cognitive Science & Psychology — 34 citations into it

Whom Jane X. Wang trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Alex Graves
5. Volodymyr Mnih
6. Yoshua Bengio
7. Diederik P. Kingma
8. Sepp Hochreiter
9. Alex Krizhevsky
10. Radford M. Neal

Who trusts Jane X. Wang overall

1. Chelsea Finn
2. Sergey Levine
3. Yuxi Li
4. Pieter Abbeel
5. Abhishek Gupta
6. Jeff Clune
7. Yoshua Bengio
8. Samuel Ritter
9. Tianhe Yu
10. Luckeciano Carvalho Melo

Papers from Jane X. Wang's perspective

Ordered by how strongly the researchers Jane X. Wang 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning to reinforcement learn (2016) — 1,097 citations
• Can language models learn from explanations in context? (2022) — 383 citations
• Causal Reasoning from Meta-reinforcement Learning (2019) — 138 citations
• Causally Correct Partial Models for Reinforcement Learning (2020) — 36 citations
• Been There, Done That: Meta-Learning with Episodic Recall (2018) — 95 citations
• Semantic Exploration from Language Abstractions and Pretrained Representations (2022) — 84 citations
• Meta-learning of Sequential Strategies (2019) — 120 citations
• Meta-learning by the Baldwin effect (2018) — 42 citations
• Data Distributional Properties Drive Emergent In-Context Learning in Transformers (2022) — 389 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
• Reinforcement Learning, Fast and Slow. (2019) — 659 citations
• Prefrontal cortex as a meta-reinforcement learning system (2018) — 627 citations