Thiago Castro Ferreira
Artificial Intelligence · 2016–2020
A researcher in the Science corpus: 7 papers; trusts 90 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 95 citations into it; #11,048 of 35,345 by trust here, #12,083 by citations
- Natural Language Processing — 92 citations into it; #3,454 of 5,688 by trust here, #3,282 by citations
- Evaluation & Benchmarks — 25 citations into it
- Computer Vision and Pattern Recognition — 20 citations into it
- Natural Language Generation — 11 citations into it; #23 of 199 by trust here, #40 by citations
- Model Architectures — 8 citations into it
- Computational Linguistics & Semantics — 5 citations into it
Whom Thiago Castro Ferreira trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ehud Reiter
- Claire Gardent
- Kyunghyun Cho
- Yoshua Bengio
- Rico Sennrich
- Philipp Koehn
- Franz Joseph Och
- Dzmitry Bahdanau
- Geoffrey E. Hinton
- Ilya Sutskever
Who trusts Thiago Castro Ferreira overall
- Fahime Same
- Chao Zhao
- Mihir Kale
- Yixuan Su
- Guanyi Chen
- Leonardo F. R. Ribeiro
- Hamza Harkous
- Sebastian Gehrmann
- Emiel van Miltenburg
- Claire Gardent
Papers from Thiago Castro Ferreira's perspective
Ordered by how strongly the researchers Thiago Castro Ferreira trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
Their papers
- Neural data-to-text generation: A comparison between pipeline and end-to-end architectures (2019) — 151 citations
- Enriching the WebNLG corpus (2018) — 59 citations
- The Third Multilingual Surface Realisation Shared Task (SR’20): Overview and Evaluation Results (2020)
- The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020) (2020)
- RDF2PT: Generating Brazilian Portuguese Texts from RDF Data (2018) — 14 citations
- Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation (2017) — 36 citations
- Towards more variation in text generation: Developing and evaluating variation models for choice of referential form (2016) — 30 citations