Daniel Fernando Campos
Artificial Intelligence · 2016–2025
A researcher in the Science corpus: 9 papers; trusts 79 researchers and is trusted by 48 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 153 citations into it; #6,712 of 35,345 by trust here, #6,720 by citations
- Natural Language Processing — 88 citations into it
- Information Retrieval & Search — 64 citations into it; #211 of 1,022 by trust here, #59 by citations
- Computer Vision and Pattern Recognition — 58 citations into it; #21,212 of 21,575 by trust here, #12,419 by citations
- Information Systems — 49 citations into it
- Evaluation & Benchmarks — 44 citations into it; #501 of 3,147 by trust here, #165 by citations
- Large Language Models — 29 citations into it
- Signal Processing — 12 citations into it
Whom Daniel Fernando Campos trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Bhaskar Mitra
- Geoffrey E. Hinton
- Ilya Sutskever
- Yoshua Bengio
- Sepp Hochreiter
- Kaiming He
- Chin-Yew Lin
- Nick Craswell
- Alex Krizhevsky
- Diederik P. Kingma
Who trusts Daniel Fernando Campos overall
- Sebastian Hofstätter
- Luyu Gao
- Jimmy J. Lin
- Bhaskar Mitra
- Zhuyun Dai
- Rodrigo Nogueira
- Omar Khattab
- Nick Craswell
- Rajarshee Mitra
- Jingtao Zhan
Papers from Daniel Fernando Campos's perspective
Ordered by how strongly the researchers Daniel Fernando Campos trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation (2020) — 398 citations
- Overview of the TREC 2019 deep learning track (2020) — 697 citations
- MS MARCO: Benchmarking Ranking Models in the Large-Data Regime (2021) — 85 citations
- TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime (2021) — 73 citations
- Overview of the TREC 2021 Deep Learning Track (2025) — 91 citations
- Overview of the TREC 2020 Deep Learning Track (2021) — 531 citations
- On the Reliability of Test Collections for Evaluating Systems of Different Types (2020) — 21 citations
- MS MARCO: A Human Generated MAchine Reading COmprehension Dataset (2016) — 524 citations
- MS MARCO: A Human Generated MAchine Reading COmprehension Dataset (2016) — 3,491 citations