Ruizhou Ding
Computer Vision and Pattern Recognition · 2017–2019
A researcher in the Science corpus: 4 papers; trusts 169 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 75 citations into it
- Artificial Intelligence — 72 citations into it
- Efficiency & Compression — 46 citations into it
- Model Architectures — 21 citations into it
- Neural Architecture Search & AutoML — 19 citations into it
- Optimization & Training — 13 citations into it
- Management Science and Operations Research — 6 citations into it
Whom Ruizhou Ding trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Yoshua Bengio
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Quoc V. Le
- Barret Zoph
- Dimitrios Stamoulis
- Matthieu Courbariaux
- Karen Simonyan
Who trusts Ruizhou Ding overall
Papers from Ruizhou Ding's perspective
Ordered by how strongly the researchers Ruizhou Ding 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours (2019) — 291 citations
- LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks (2017) — 38 citations
- FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference (2019) — 26 citations
- Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization (2019) — 37 citations