Daniel Nevo

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 53 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it; #22,773 of 35,345 by trust here, #18,586 by citations
• Natural Language Processing — 68 citations into it; #3,664 of 5,688 by trust here, #2,220 by citations
• Safety, Robustness & Interpretability — 51 citations into it; #1,897 of 2,251 by trust here, #1,119 by citations
• Statistics and Probability — 34 citations into it; #8,359 of 12,932 by trust here, #10,275 by citations
• Causal Inference & Discovery — 22 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Algorithms & Complexity — 14 citations into it
• Computational Theory and Mathematics — 12 citations into it

Whom Daniel Nevo trusts overall

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

1. Nicolai Meinshausen
2. Hui Zou
3. Robert Tibshirani
4. Leo Breiman
5. Peter Buhlmann
6. Trevor J. Hastie
7. Hirotugu Akaike
8. Jerome H. Friedman
9. Ming Yuan
10. Peng Zhao

Who trusts Daniel Nevo overall

1. Lesia Semenova
2. Cynthia Rudin
3. Aaron Fisher
4. Yonatan Belinkov
5. Jiayun Dong
6. Kevin Meng

Papers from Daniel Nevo's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. ImageNet classification with deep convolutional neural networks
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. Regression Shrinkage and Selection via the Lasso
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias (2020) — 211 citations
• Identifying a Minimal Class of Models for High-dimensional Data (2015) — 19 citations
• Investigating Gender Bias in Language Models Using Causal Mediation Analysis (2020) — 725 citations