Eliyahu Kiperwasser

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 354 researchers and is trusted by 67 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #3,338 of 35,345 by trust here, #8,893 by citations
• Natural Language Processing — 71 citations into it; #839 of 5,688 by trust here, #1,975 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #3,702 of 21,575 by trust here, #6,326 by citations
• Model Architectures — 19 citations into it; #346 of 2,278 by trust here, #1,038 by citations
• Evaluation & Benchmarks — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Molecular Biology — 8 citations into it
• Information Extraction — 6 citations into it

Whom Eliyahu Kiperwasser trusts overall

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

1. Yoav Goldberg
2. Diederik P. Kingma
3. Ilya Sutskever
4. Richard Socher
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. Alex Graves
8. Christopher D. Manning
9. Oriol Vinyals
10. Tomas Mikolov

Who trusts Eliyahu Kiperwasser overall

1. André F. T. Martins
2. Barbara Plank
3. Xuezhe Ma
4. Yoav Goldberg
5. Miguel Ballesteros
6. Sanyuan Chen
7. Noah A. Smith
8. Timothy Dozat
9. Julia Kreutzer
10. Chris Dyer

Papers from Eliyahu Kiperwasser's perspective

Ordered by how strongly the researchers Eliyahu Kiperwasser 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations (2016) — 688 citations
• Easy-First Dependency Parsing with Hierarchical Tree LSTMs (2016) — 67 citations
• Scheduled Multi-Task Learning: From Syntax to Translation (2018) — 87 citations