Riza Theresa Batista-Navarro
Artificial Intelligence · 2015–2020
A researcher in the Science corpus: 2 papers; trusts 122 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 93 citations into it
- Natural Language Processing — 71 citations into it
- Evaluation & Benchmarks — 44 citations into it
- Computer Vision and Pattern Recognition — 33 citations into it
- Safety, Robustness & Interpretability — 17 citations into it
- Information Systems — 7 citations into it
- Information Retrieval & Search — 5 citations into it
Whom Riza Theresa Batista-Navarro trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Robin Jia
- Kaiming He
- Yann LeCun
- Percy Liang
- Christopher D. Manning
Papers from Riza Theresa Batista-Navarro's perspective
Ordered by how strongly the researchers Riza Theresa Batista-Navarro trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- 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
- Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models (2020) — 18 citations
- The CHEMDNER corpus of chemicals and drugs and its annotation principles (2015) — 444 citations