Di Niu
Artificial Intelligence · 2013–2020
A researcher in the Science corpus: 5 papers; trusts 26 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 86 citations into it; #31,498 of 35,345 by trust here, #18,186 by citations
- Natural Language Processing — 22 citations into it
- Computer Networks and Communications — 20 citations into it
- Privacy & Security — 19 citations into it
- Safety, Robustness & Interpretability — 9 citations into it
- Efficiency & Compression — 5 citations into it
Whom Di Niu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cynthia Dwork
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Mu Li
- Alex Krizhevsky
- Yann LeCun
- Ilya Sutskever
- Richard S. Sutton
- Reza Shokri
Who trusts Di Niu overall
Papers from Di Niu's perspective
Ordered by how strongly the researchers Di Niu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Learning to Generate Questions by LearningWhat not to Generate (2019) — 106 citations
- Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications (2013) — 366 citations
- Optimizing Federated Learning on Non-IID Data with Reinforcement Learning (2020) — 1,034 citations
- Learning Privately over Distributed Features: An ADMM Sharing Approach (2019) — 39 citations
- FDML: A Collaborative Machine Learning Framework for Distributed Features (2019) — 196 citations