Kangcheng Hou
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 40 citations into it
- Computer Vision and Pattern Recognition — 24 citations into it
- Graph Learning — 20 citations into it
- Optimization & Training — 10 citations into it
- Statistical Learning Theory — 9 citations into it
- Representation & Self-Supervised Learning — 5 citations into it
Whom Kangcheng Hou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Simon Shaolei Du
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Peter L. Bartlett
- Sanjeev Arora
- Keyulu Xu
Who trusts Kangcheng Hou overall
Papers from Kangcheng Hou's perspective
Ordered by how strongly the researchers Kangcheng Hou 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)
- 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
- Biological Sequence Modeling with Convolutional Kernel Networks (2017) — 178 citations
- Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (2019) — 311 citations