Yair Feldman

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 103 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #12,367 of 35,345 by trust here, #19,873 by citations
• Natural Language Processing — 34 citations into it; #2,356 of 5,688 by trust here, #3,897 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #8,290 of 21,575 by trust here, #13,269 by citations
• Information Retrieval & Search — 13 citations into it; #435 of 1,022 by trust here, #602 by citations
• Evaluation & Benchmarks — 9 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Yair Feldman trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Matthew D. Zeiler
4. Ilya Sutskever
5. Jason Aaron Edward Weston
6. Alex Krizhevsky
7. Kyunghyun Cho
8. Christopher D. Manning
9. Tomas Mikolov
10. Diederik P. Kingma

Who trusts Yair Feldman overall

1. Omar Khattab
2. Yuyu Zhang
3. Peng Qi
4. Akari Asai
5. Bhuwan Dhingra
6. Fengbin Zhu
7. Jinjie Ni
8. Chen Zhao
9. Yuqing Xie
10. Christopher Potts

Papers from Yair Feldman's perspective

Ordered by how strongly the researchers Yair Feldman 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Multi-Hop Paragraph Retrieval for Open-Domain Question Answering (2019) — 105 citations