William H. Guss

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 36 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it
• Natural Language Processing — 45 citations into it; #3,234 of 5,688 by trust here, #5,593 by citations
• Reinforcement Learning — 39 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Control and Systems Engineering — 8 citations into it

Whom William H. Guss trusts overall

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

1. David R Ha
2. Volodymyr Mnih
3. Geoffrey E. Hinton
4. Sainbayar Sukhbaatar
5. Alex Krizhevsky
6. Yoshua Bengio
7. Diederik P. Kingma
8. Marc G. Bellemare
9. Sergey Levine
10. Richard S. Sutton

Who trusts William H. Guss overall

1. Nikita Moghe
2. Ashwin Ram
3. Linxi (Jim) Fan
4. Siddharth Arora
5. Zihao Wang
6. Andy Zhou
7. William Harvey
8. Suman Banerjee
9. Guanzhi Wang
10. Rohit Prasad

Papers from William H. Guss's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Evaluating Large Language Models Trained on Code (2021) — 10,663 citations
• The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors (2021) — 73 citations
• MineRL: A Large-Scale Dataset of Minecraft Demonstrations (2019) — 305 citations
• Eigen : A Step Towards Conversational AI (2017)