Nadav Oved

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 268 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 180 citations into it; #21,008 of 35,345 by trust here, #19,873 by citations
• Natural Language Processing — 124 citations into it; #3,878 of 5,688 by trust here, #4,183 by citations
• Computer Vision and Pattern Recognition — 52 citations into it; #14,295 of 21,575 by trust here, #13,815 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Large Language Models — 34 citations into it
• Safety, Robustness & Interpretability — 31 citations into it; #1,510 of 2,251 by trust here, #1,857 by citations
• Evaluation & Benchmarks — 20 citations into it
• General Social Sciences — 17 citations into it

Whom Nadav Oved trusts overall

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

1. Diederik P. Kingma
2. Yann LeCun
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Krizhevsky
6. Ilya Sutskever
7. Jimmy Ba
8. Jacob Devlin
9. Sepp Hochreiter
10. Ruslan Salakhutdinov

Who trusts Nadav Oved overall

1. Yonatan Belinkov
2. Yaakov Ophir
3. Amir Feder
4. Leonard Bereska
5. Refael Tikochinski
6. Pengfei Liu
7. Yanai Elazar
8. Roi Reichart
9. Yongchao Zhou

Papers from Nadav Oved's perspective

Ordered by how strongly the researchers Nadav Oved 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• CausaLM: Causal Model Explanation Through Counterfactual Language Models (2020) — 193 citations
• PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains (2021) — 103 citations
• Predicting In-Game Actions from Interviews of NBA Players (2019) — 29 citations
• PADA: A Prompt-based Autoregressive Approach for Adaptation to Unseen Domains (2021) — 70 citations
• Predicting In-game Actions From the Language of NBA Players (2019)