Ohad Rubin

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 124 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #13,708 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 47 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it; #21,176 of 21,575 by trust here, #9,563 by citations
• Large Language Models — 19 citations into it
• Information Retrieval & Search — 11 citations into it
• Prompting & Reasoning — 11 citations into it; #342 of 359 by trust here, #225 by citations
• Information Systems — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Ohad Rubin trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Jacob Devlin
5. Sepp Hochreiter
6. Ilya Sutskever
7. Alex Krizhevsky
8. J. Urgen Schmidhuber
9. Yann LeCun
10. Ashish Vaswani

Who trusts Ohad Rubin overall

1. Qingxiu Dong
2. Torsten Scholak
3. Haoyang Li
4. Jonathan Herzig
5. Aman Madaan
6. Yingqiang Ge
7. Peter Shaw
8. Sweta Agrawal
9. Emmanouil Antonios Platanios
10. Andrew Zhao

Papers from Ohad Rubin's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. ImageNet classification with deep convolutional neural networks
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Gradient-based learning applied to document recognition
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Learning To Retrieve Prompts for In-Context Learning (2021) — 945 citations
• SmBoP: Semi-autoregressive Bottom-up Semantic Parsing (2020) — 193 citations