Yi Zhang
Information Systems · with Zhang · 2014–2018
A researcher in the Science corpus: 5 papers; trusts 48 researchers and is trusted by 24 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 58 citations into it; #1,036 of 8,771 by trust here, #786 by citations
- Artificial Intelligence — 51 citations into it; #7,431 of 35,345 by trust here, #7,471 by citations
- Computer Vision and Pattern Recognition — 20 citations into it
- Natural Language Processing — 18 citations into it
- Recommender Systems — 16 citations into it
- Management Science and Operations Research — 13 citations into it
- Signal Processing — 13 citations into it
- Computational Mechanics — 12 citations into it
Whom Yi Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ronald J. Williams
- Ruslan Salakhutdinov
- Richard S. Sutton
- Yehuda Koren
- Geoffrey E. Hinton
- Yoshua Bengio
- Yongfeng Zhang
- Antoine Bordes
- E.J. Candes
- Diederik P. Kingma
Who trusts Yi Zhang overall
- Yongfeng Zhang
- Xu Chen
- Xiangnan He
- Wenqiang Lei
- Chongming Gao
- M. M. Afsar
- Kun Zhou
- Dietmar Jannach
- Behnoush Abdollahi
- Qingyao Ai
Papers from Yi Zhang's perspective
Ordered by how strongly the researchers Yi Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (2014) — 858 citations
- Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables (2014) — 18 citations
- Conversational Recommender System (2018) — 511 citations
- Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis (2015) — 80 citations
- 2015 Ieee International Conference on Big Data (big Data) Task-based Recommendation on a Web-scale