R. Fern'andez

Artificial Intelligence · with Bernardi · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 114 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #17,070 of 35,345 by trust here, #5,153 by citations
• Natural Language Processing — 75 citations into it; #3,768 of 5,688 by trust here, #5,296 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Cultural Studies — 16 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Evaluation & Benchmarks — 11 citations into it; #1,866 of 3,147 by trust here, #880 by citations
• Statistical and Nonlinear Physics — 11 citations into it
• Information Systems — 9 citations into it

Whom R. Fern'andez trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Tomas Mikolov
4. Yoshua Bengio
5. Rediet Abebe
6. Jimmy Ba
7. Ilya Sutskever
8. Alex Krizhevsky
9. Zeerak Talat
10. Yann LeCun

Who trusts R. Fern'andez overall

1. Yi-Ling Chung
2. Maarten Sap
3. Serra Sinem Tekiroğlu
4. Zixuan Ke

Papers from R. Fern'andez's perspective

Ordered by how strongly the researchers R. Fern'andez trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• The LAMBADA dataset: Word prediction requiring a broad discourse context (2016) — 1,160 citations
• Examining a hate speech corpus for hate speech detection and popularity prediction (2018) — 37 citations
• Analysing Lexical Semantic Change with Contextualised Word Representations (2020) — 215 citations
• Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering (2019) — 58 citations
• Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat (2018) — 51 citations
• Semantic Variation in Online Communities of Practice (2018) — 43 citations