Bowen Tan

Artificial Intelligence · with Hu · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 307 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 190 citations into it
• Natural Language Processing — 150 citations into it
• Computer Vision and Pattern Recognition — 68 citations into it
• Evaluation & Benchmarks — 39 citations into it
• Reinforcement Learning — 38 citations into it
• Large Language Models — 19 citations into it
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Bowen Tan trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Kaiming He
7. Yann LeCun
8. Alex Graves
9. Ronald J. Williams
10. Richard Socher

Who trusts Bowen Tan overall

1. Christian Geishauser
2. Mingkai Deng
3. Sebastian Gehrmann
4. Varun Kumar
5. Zhi Chen
6. Ximing Lu
7. Yuheng Zha
8. Silei Xu
9. Sen Wu
10. Ruisheng Cao

Papers from Bowen Tan's perspective

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

Their papers

• Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks (2020) — 144 citations
• Learning Data Manipulation for Augmentation and Weighting (2019) — 126 citations
• Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation (2021) — 83 citations
• Text Generation with Efficient (Soft) Q-Learning (2021) — 32 citations
• Progressive Generation of Long Text with Pretrained Language Models (2021) — 107 citations
• Structured Dialogue Policy with Graph Neural Networks (2018) — 33 citations