Daniel Preotiuc-Pietro

Artificial Intelligence · 2013–2022

A researcher in the Science corpus: 9 papers; trusts 108 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #27,470 of 35,345 by trust here, #18,586 by citations
• Natural Language Processing — 58 citations into it
• Social Psychology — 29 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Sociology and Political Science — 25 citations into it; #19,832 of 22,756 by trust here, #9,840 by citations
• Statistical and Nonlinear Physics — 25 citations into it; #7,283 of 10,127 by trust here, #4,137 by citations
• Representation & Self-Supervised Learning — 21 citations into it
• Clinical Psychology — 19 citations into it

Whom Daniel Preotiuc-Pietro trusts overall

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

1. Tomas Mikolov
2. Radford M. Neal
3. David M. Blei
4. Geoffrey E. Hinton
5. Andrew Gordon Wilson
6. Andrew Y. Ng
7. Yoshua Bengio
8. Ilya Sutskever
9. Michael I. Jordan
10. Marco Pennacchiotti

Who trusts Daniel Preotiuc-Pietro overall

1. Yulia Tsvetkov
2. Carolina Scarton
3. Kokil Jaidka
4. Lyle Ungar
5. Hamed Jelodar
6. Mohammed Ali Al-Garadi
7. Ilias Chalkidis
8. Michael M. Tadesse
9. Ye Liu
10. Nikolaos Aletras

Papers from Daniel Preotiuc-Pietro's perspective

Ordered by how strongly the researchers Daniel Preotiuc-Pietro 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. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Analyzing Biases in Human Perception of User Age and Gender from Text (2016) — 54 citations
• Dataless Knowledge Fusion by Merging Weights of Language Models (2022) — 406 citations
• The role of personality, age, and gender in tweeting about mental illness (2015) — 166 citations
• Beyond Binary Labels: Political Ideology Prediction of Twitter Users (2017) — 282 citations
• Studying User Income through Language, Behaviour and Affect in Social Media (2015) — 216 citations
• Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective (2016) — 723 citations
• User-Level Race and Ethnicity Predictors from Twitter Text (2018) — 78 citations
• Predicting and Characterising User Impact on Twitter (2014) — 83 citations
• A temporal model of text periodicities using Gaussian Processes (2013) — 51 citations