Ching-Hao Wang
Artificial Intelligence · 2018
A researcher in the Science corpus: 1 paper; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 79 citations into it
- Computer Vision and Pattern Recognition — 32 citations into it
- Optimization & Training — 25 citations into it
- Statistics and Probability — 21 citations into it
- Probabilistic & Bayesian Methods — 17 citations into it
- Statistical and Nonlinear Physics — 16 citations into it
- Representation & Self-Supervised Learning — 15 citations into it
- Scaling & Learning Theory — 9 citations into it
Whom Ching-Hao Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Radford M. Neal
- L. Eon Bottou
- J. Urgen Schmidhuber
- Yann LeCun
- Ilya Sutskever
- Alex Krizhevsky
- Jerome H. Friedman
Papers from Ching-Hao Wang's perspective
Ordered by how strongly the researchers Ching-Hao Wang 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
- 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
- A high-bias, low-variance introduction to Machine Learning for physicists (2018) — 1,003 citations