Adam Yala

Artificial Intelligence · with Barzilay · 2016–2019

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it; #12,286 of 35,345 by trust here, #12,083 by citations
• Natural Language Processing — 11 citations into it; #2,352 of 5,688 by trust here, #3,014 by citations
• Reinforcement Learning — 5 citations into it; #1,000 of 2,242 by trust here, #1,280 by citations

Whom Adam Yala trusts overall

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

1. Kaiming He
2. Mukund Sundararajan
3. Richard Socher
4. Geoffrey E. Hinton
5. X. Zhang
6. Yoshua Bengio
7. Hoifung Poon
8. Fei Wu
9. Alex Krizhevsky
10. Eugene Agichtein

Who trusts Adam Yala overall

1. Shiyu Chang
2. Rodrigo Nogueira
3. Yueming Sun
4. Mo Yu
5. Dirk Weissenborn
6. Johannes Welbl
7. Dipendra Kumar Misra
8. Eunsol Choi
9. Reabal Najjar
10. Jing Li

Papers from Adam Yala's perspective

Ordered by how strongly the researchers Adam Yala 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning (2016) — 153 citations
• A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. (2019) — 630 citations