Elisabetta Fersini
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 3 papers; trusts 14 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 25 citations into it; #19,054 of 35,345 by trust here, #9,968 by citations
- Natural Language Processing — 21 citations into it; #4,885 of 5,688 by trust here, #2,422 by citations
- Safety, Robustness & Interpretability — 11 citations into it; #2,147 of 2,251 by trust here, #1,089 by citations
Whom Elisabetta Fersini trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Fabrizio Sebastiani
- Radim Rehurek
- Zeerak Talat
- Richard I. Parker
- Svetlana Kiritchenko
- Lucas Dixon
- Chikashi Nobata
- Geoffrey E. Hinton
Who trusts Elisabetta Fersini overall
Papers from Elisabetta Fersini's perspective
Ordered by how strongly the researchers Elisabetta Fersini trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- GloVe: Global Vectors for Word Representation
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (2019) — 1,079 citations
- Unintended Bias in Misogyny Detection (2019) — 87 citations
- Automatic Identification and Classification of Misogynistic Language on Twitter (2018) — 218 citations