Zhenyu A. Liao
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 5 papers; trusts 279 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 115 citations into it; #27,791 of 35,345 by trust here, #22,463 by citations
- Computer Vision and Pattern Recognition — 69 citations into it; #17,153 of 21,575 by trust here, #19,310 by citations
- Safety, Robustness & Interpretability — 35 citations into it
- Optimization & Training — 29 citations into it
- Model Architectures — 23 citations into it
- Statistics and Probability — 23 citations into it; #8,510 of 12,932 by trust here, #7,165 by citations
- Efficiency & Compression — 16 citations into it; #992 of 1,264 by trust here, #1,215 by citations
- Scaling & Learning Theory — 11 citations into it
Whom Zhenyu A. Liao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- Alex Krizhevsky
- Radford M. Neal
- Geoffrey E. Hinton
- L. Eon Bottou
- Yoshua Bengio
- Andrew M. Saxe
- Kaiming He
- Ilya Sutskever
- Diederik P. Kingma
Who trusts Zhenyu A. Liao overall
Papers from Zhenyu A. Liao's perspective
Ordered by how strongly the researchers Zhenyu A. Liao 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
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- The Dynamics of Learning: A Random Matrix Approach (2018) — 44 citations
- AdaBits: Neural Network Quantization With Adaptive Bit-Widths (2019) — 152 citations
- Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses (2019) — 59 citations
- A Random Matrix Approach to Neural Networks (2017) — 194 citations
- Almost Global Convergence to Global Minima for Gradient Descent in Deep Linear Networks (2018)