Tomer Wolfson

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 131 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #35,121 of 35,345 by trust here, #12,761 by citations
• Natural Language Processing — 80 citations into it; #5,653 of 5,688 by trust here, #3,107 by citations
• Computer Vision and Pattern Recognition — 49 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Information Systems — 11 citations into it
• Information Retrieval & Search — 9 citations into it

Whom Tomer Wolfson trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Luke S. Zettlemoyer
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alex Krizhevsky
7. Kaiming He
8. Yann LeCun
9. Jacob Devlin
10. Alex Graves

Who trusts Tomer Wolfson overall

1. Tushar Khot
2. Zhengzhong Liang
3. Daniel Khashabi
4. James Thorne
5. Mor Geva
6. Sanjay Subramanian
7. Jiaqi Guo
8. Kyle Richardson
9. Vladimir Karpukhin
10. Harsh Trivedi

Papers from Tomer Wolfson's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
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

• Break It Down: A Question Understanding Benchmark (2020) — 228 citations
• Obtaining Faithful Interpretations from Compositional Neural Networks (2020) — 43 citations
• Making Retrieval-Augmented Language Models Robust to Irrelevant Context (2023) — 405 citations