Despoina Chatzakou

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #23,309 of 35,345 by trust here, #17,110 by citations
• Natural Language Processing — 34 citations into it; #5,520 of 5,688 by trust here, #2,172 by citations
• Information Systems — 31 citations into it
• Safety, Robustness & Interpretability — 19 citations into it; #2,156 of 2,251 by trust here, #856 by citations

Whom Despoina Chatzakou trusts overall

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

1. Alex Hai Wang
2. J. Fleiss
3. Gonzalo Navarro
4. Ji-Hyun Kim
5. Jesse Davis
6. J. Landis
7. Nemanja Djuric
8. Karthik Dinakar
9. Tomas Mikolov
10. Chikashi Nobata

Who trusts Despoina Chatzakou overall

1. Svetlana Kiritchenko
2. Rui Cao
3. Antigoni-Maria Founta
4. Bertie Vidgen
5. Isar Nejadgholi
6. Roy Ka-Wei Lee
7. Alexandra Olteanu
8. Maarten Sap
9. Thanh Thuy Trang Tran
10. Tuan-Anh Hoang

Papers from Despoina Chatzakou's perspective

Ordered by how strongly the researchers Despoina Chatzakou 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
3. Efficient Estimation of Word Representations in Vector Space
4. GloVe: Global Vectors for Word Representation
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet classification with deep convolutional neural networks
9. Gradient-based learning applied to document recognition
10. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)

Their papers

• Mean Birds: Detecting Aggression and Bullying on Twitter (2017) — 435 citations
• Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior (2018) — 824 citations
• A Unified Deep Learning Architecture for Abuse Detection (2018) — 250 citations