Min Zhang
Artificial Intelligence · with Ma · 2008–2020
A researcher in the Science corpus: 20 papers; trusts 172 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 193 citations into it; #6,134 of 35,345 by trust here, #5,252 by citations
- Information Systems — 181 citations into it; #695 of 8,771 by trust here, #400 by citations
- Computer Vision and Pattern Recognition — 85 citations into it
- Natural Language Processing — 64 citations into it; #4,872 of 5,688 by trust here, #5,092 by citations
- Information Retrieval & Search — 62 citations into it
- Recommender Systems — 51 citations into it
- Management Science and Operations Research — 34 citations into it
- Signal Processing — 28 citations into it
Whom Min Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yehuda Koren
- Ruslan Salakhutdinov
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Daniel D. Lee
- Nati Srebro
- Yongfeng Zhang
- Alex Krizhevsky
- Ilya Sutskever
Who trusts Min Zhang overall
- Yongfeng Zhang
- Xu Chen
- Zhiyong Cheng
- Hanxiong Chen
- Chenliang Li
- Xiangnan He
- Xiting Wang
- Yiqun Liu
- Guokun Lai
- Qingyao Ai
Papers from Min Zhang's perspective
Ordered by how strongly the researchers Min Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Automatic online news issue construction in web environment (2008) — 117 citations
- Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (2014) — 858 citations
- Localized matrix factorization for recommendation based on matrix block diagonal forms (2013) — 78 citations
- Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables (2014) — 18 citations
- An Analysis of BERT in Document Ranking (2020) — 55 citations
- Leveraging Passage-level Cumulative Gain for Document Ranking (2020) — 40 citations
- RepBERT: Contextualized Text Embeddings for First-Stage Retrieval (2020) — 140 citations
- Preference-based Evaluation Metrics for Web Image Search (2020) — 14 citations
- Neural Logic Networks (2019) — 17 citations
- In2Rec: Influence-based Interpretable Recommendation (2019) — 11 citations
- Human Behavior Inspired Machine Reading Comprehension (2019) — 44 citations
- Jointly Learning Explainable Rules for Recommendation with Knowledge Graph (2019) — 246 citations
- Neural Attentional Rating Regression with Review-level Explanations (2018) — 644 citations
- SogouT-16: A New Web Corpus to Embrace IR Research (2017) — 14 citations
- Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews (2016) — 174 citations
- Time-Aware Click Model (2016) — 42 citations
- Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis (2015) — 80 citations
- Do users rate or review?: boost phrase-level sentiment labeling with review-level sentiment classification (2014) — 122 citations
- Improve collaborative filtering through bordered block diagonal form matrices (2013) — 37 citations
- 2015 Ieee International Conference on Big Data (big Data) Task-based Recommendation on a Web-scale