Sudha Rao
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 4 papers; trusts 236 researchers and is trusted by 36 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 59 citations into it; #6,266 of 35,345 by trust here, #8,007 by citations
- Natural Language Processing — 57 citations into it; #1,597 of 5,688 by trust here, #1,975 by citations
- Computer Vision and Pattern Recognition — 24 citations into it; #6,898 of 21,575 by trust here, #10,500 by citations
- Model Architectures — 13 citations into it
- Evaluation & Benchmarks — 9 citations into it
- Representation & Self-Supervised Learning — 7 citations into it
- Natural Language Generation — 6 citations into it
Whom Sudha Rao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sepp Hochreiter
- Yoshua Bengio
- Ilya Sutskever
- Ronald J. Williams
- Rico Sennrich
- Geoffrey E. Hinton
- J. Urgen Schmidhuber
- Dzmitry Bahdanau
- Kyunghyun Cho
- Richard Socher
Who trusts Sudha Rao overall
- Anna Wegmann
- ZiYun Wang
- Fatemehsadat Mireshghallah
- Eleftheria Briakou
- Sachin Kumar
- Xinyao Ma
- Hamed Zamani
- Yi Zhang
- Jie Gui
- Ruibo Liu
Papers from Sudha Rao's perspective
Ordered by how strongly the researchers Sudha Rao 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
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- 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)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task (2017) — 95 citations
- Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information (2018) — 227 citations
- Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer (2018) — 442 citations
- Answer-based Adversarial Training for Generating Clarification Questions (2019) — 129 citations