QUAN LIU

Artificial Intelligence · Signal Transduction and Targeted Therapy · 2014–2023

A researcher in the Science corpus: 12 papers; trusts 179 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 164 citations into it; #19,836 of 35,345 by trust here, #13,544 by citations
• Natural Language Processing — 147 citations into it; #5,288 of 5,688 by trust here, #3,826 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Representation & Self-Supervised Learning — 25 citations into it; #2,923 of 3,145 by trust here, #2,587 by citations
• Signal Processing — 20 citations into it
• Management Science and Operations Research — 17 citations into it
• Model Architectures — 16 citations into it

Whom QUAN LIU trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Tomas Mikolov
6. Alex Krizhevsky
7. Jason Aaron Edward Weston
8. Yann LeCun
9. Alex Graves
10. Ronan Collobert

Who trusts QUAN LIU overall

1. Ali Emami
2. Hui Jiang
3. Shuohang Wang
4. Trieu H. Trinh
5. Yang Peng
6. Zhenhua Ling
7. Xiao-Dan Zhu
8. Adam Trischler
9. Sheng Zhang
10. Rakshit S. Trivedi

Papers from QUAN LIU's perspective

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

Their papers

• Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints (2015) — 141 citations
• Probabilistic Reasoning via Deep Learning: Neural Association Models (2016) — 103 citations
• A Feature Fusion Based Forecasting Model for Financial Time Series (2014) — 54 citations
• Macrophages in immunoregulation and therapeutics (2023) — 1,870 citations
• Ultrathin two-dimensional MXene membrane for pervaporation desalination (2018) — 332 citations
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network (2019) — 350 citations
• Exploring End-to-End Differentiable Natural Logic Modeling (2020) — 15 citations
• Program Enhanced Fact Verification with Verbalization and Graph Attention Network (2020) — 58 citations
• Building Sequential Inference Models for End-to-End Response Selection (2018) — 6 citations
• Study on the effect of aging on physical properties of asphalt binder from a microscale perspective (2018) — 210 citations
• Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge (2016) — 15 citations
• Combing Context and Commonsense Knowledge Through Neural Networks for Solving Winograd Schema Problems (2016) — 35 citations