Jinfeng Rao

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 11 papers; trusts 195 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 366 citations into it; #26,442 of 35,345 by trust here, #6,266 by citations
• Natural Language Processing — 229 citations into it; #5,416 of 5,688 by trust here, #2,069 by citations
• Computer Vision and Pattern Recognition — 159 citations into it
• Evaluation & Benchmarks — 103 citations into it
• Model Architectures — 68 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Optimization & Training — 13 citations into it
• Molecular Biology — 9 citations into it

Whom Jinfeng Rao trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Diederik P. Kingma
5. Dzmitry Bahdanau
6. Alex Krizhevsky
7. Richard Socher
8. Christopher D. Manning
9. Sepp Hochreiter
10. Oriol Vinyals

Who trusts Jinfeng Rao overall

1. Yi Tay
2. Mostafa Dehghani
3. Xinnuo Xu
4. Daniel Kang
5. Peyman Heidari
6. Ondrej Dusek
7. Emily Sheng
8. Symon Jory Stevens-Guille

Papers from Jinfeng Rao's perspective

Ordered by how strongly the researchers Jinfeng Rao trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Long Range Arena: A Benchmark for Efficient Transformers (2020) — 917 citations
• ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning (2021) — 240 citations
• Transcending Scaling Laws with 0.1% Extra Compute (2022) — 78 citations
• Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers (2021) — 152 citations
• Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue (2019) — 87 citations
• Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling? (2022) — 130 citations
• A Tree-to-Sequence Model for Neural NLG in Task-Oriented Dialog (2019) — 14 citations
• UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement (2016) — 31 citations
• Scale Efficiently: Insights from Pretraining and Finetuning Transformers (2022) — 70 citations
• Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (2019) — 71 citations
• Automatically Extracting High-Quality Negative Examples for Answer Selection in Question Answering (2017) — 10 citations