Jinpeng Wang

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 6 papers; trusts 141 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #27,164 of 35,345 by trust here, #15,236 by citations
• Natural Language Processing — 76 citations into it; #5,593 of 5,688 by trust here, #3,421 by citations
• Computer Vision and Pattern Recognition — 52 citations into it; #19,723 of 21,575 by trust here, #5,507 by citations
• Multimodal Learning — 23 citations into it; #1,174 of 1,211 by trust here, #561 by citations
• Management Science and Operations Research — 14 citations into it
• Model Architectures — 7 citations into it

Whom Jinpeng Wang trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Matthew D. Zeiler
6. Tomas Mikolov
7. Alex Krizhevsky
8. Yann LeCun
9. Kyunghyun Cho
10. Kaiming He

Who trusts Jinpeng Wang overall

1. Fatemeh Hemmatian
2. Nikhita Vedula
3. Mohammad Karim Sohrabi
4. Swati Agarwal
5. Ziwei Ji
6. Ondrej Dusek
7. Nedim Lipka

Papers from Jinpeng Wang's perspective

Ordered by how strongly the researchers Jinpeng Wang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
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

• Evaluating Object Hallucination in Large Vision-Language Models (2023) — 1,825 citations
• A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (2019) — 90 citations
• Mention and Entity Description Co-Attention for Entity Disambiguation (2018) — 33 citations
• Improving Entity Linking by Modeling Latent Entity Type Information (2020) — 72 citations
• Operations Guided Neural Networks for High Fidelity Data-To-Text Generation (2018) — 21 citations
• Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets (2015) — 85 citations