Yue Ning
Artificial Intelligence · 2019
A researcher in the Science corpus: 3 papers; trusts 47 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 78 citations into it; #17,589 of 35,345 by trust here, #20,836 by citations
- Optimization & Training — 27 citations into it
- Natural Language Processing — 23 citations into it; #4,794 of 5,688 by trust here, #5,296 by citations
- Federated & Distributed Learning — 20 citations into it
- Safety, Robustness & Interpretability — 12 citations into it; #1,856 of 2,251 by trust here, #2,180 by citations
- Computer Vision and Pattern Recognition — 11 citations into it
- Information Systems — 8 citations into it
- Management Science and Operations Research — 8 citations into it
Whom Yue Ning trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jimmy Ba
- L. Eon Bottou
- Yurii E. Nesterov
- H. Brendan McMahan
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Jakub Konecný
- Alex Krizhevsky
- Ofer Dekel
Who trusts Yue Ning overall
Papers from Yue Ning's perspective
Ordered by how strongly the researchers Yue Ning 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)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- Empirical Analysis of Multi-Task Learning for Reducing Model Bias in Toxic Comment Detection (2019) — 23 citations
- Asynchronous Online Federated Learning for Edge Devices (2019) — 57 citations
- Asynchronous Online Federated Learning for Edge Devices with Non-IID Data (2019) — 480 citations