Amittai Axelrod

Artificial Intelligence · 2011–2020

A researcher in the Science corpus: 4 papers; trusts 25 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #33,387 of 35,345 by trust here, #25,040 by citations
• Natural Language Processing — 43 citations into it; #2,841 of 5,688 by trust here, #4,983 by citations
• Evaluation & Benchmarks — 27 citations into it
• Machine Translation — 11 citations into it; #114 of 292 by trust here, #182 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Large Language Models — 7 citations into it

Whom Amittai Axelrod trusts overall

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

1. Robert C. Moore
2. Franz Joseph Och
3. Kenneth Heafield
4. Philipp Koehn
5. Andreas Stolcke
6. William D. Lewis
7. Peter F. Brown
8. Stanley F. Chen
9. Alex Graves
10. Cuong Hoang

Who trusts Amittai Axelrod overall

1. Philipp Koehn
2. Nadir Durrani
3. Felix Stahlberg
4. Lucía Santamaría
5. Kyunghyun Cho
6. Marcin Junczys-Dowmunt
7. Alexandra Birch
8. Shachar Mirkin
9. Markus Freitag
10. Huda Khayrallah

Papers from Amittai Axelrod's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet classification with deep convolutional neural networks
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Data Selection with Cluster-Based Language Difference Models and Cynical Selection (2019) — 6 citations
• FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN (2020) — 140 citations
• Class-based N-gram language difference models for data selection (2015) — 16 citations
• Domain Adaptation via Pseudo In-Domain Data Selection (2011) — 579 citations