Zhuoye Ding

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 28 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it
• Information Systems — 45 citations into it; #5,538 of 8,771 by trust here, #2,912 by citations
• Reinforcement Learning — 26 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Natural Language Processing — 17 citations into it
• Recommender Systems — 8 citations into it
• Model Architectures — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Zhuoye Ding trusts overall

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

1. Longxin Lin
2. Richard S. Sutton
3. Andrew Y. Ng
4. Kalervo Järvelin
5. Andrew Turpin
6. Andrew W. Moore
7. Volodymyr Mnih
8. J. Breese
9. Geoffrey E. Hinton
10. David E. Heckerman

Who trusts Zhuoye Ding overall

1. M. M. Afsar
2. Xiangyu Zhao
3. Shoujin Wang

Papers from Zhuoye Ding's perspective

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

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

Their papers

• Deep reinforcement learning for page-wise recommendations (2018) — 442 citations
• Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (2018) — 386 citations
• Deep Reinforcement Learning for List-wise Recommendations (2017) — 195 citations
• FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning (2021) — 7 citations