Zach Wood-Doughty

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 20 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #27,208 of 35,345 by trust here, #21,878 by citations
• Natural Language Processing — 48 citations into it; #4,454 of 5,688 by trust here, #4,539 by citations
• Causal Inference & Discovery — 26 citations into it; #285 of 364 by trust here, #315 by citations
• Computer Vision and Pattern Recognition — 16 citations into it
• Statistics and Probability — 15 citations into it
• Model Architectures — 8 citations into it

Whom Zach Wood-Doughty trusts overall

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

1. Judea Pearl
2. Diederik P. Kingma
3. Sepp Hochreiter
4. Anastasios A. Tsiatis
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. Mark Dredze
8. Kyunghyun Cho
9. Stefan Wager
10. Razvan Pascanu

Who trusts Zach Wood-Doughty overall

1. Katherine A. Keith
2. Liuyi Yao
3. Amir Feder
4. David D. Jensen
5. Zhixuan Chu

Papers from Zach Wood-Doughty's perspective

Ordered by how strongly the researchers Zach Wood-Doughty trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2021) — 330 citations
• Predicting Twitter User Demographics from Names Alone (2018) — 44 citations
• Challenges of Using Text Classifiers for Causal Inference (2018) — 85 citations