Ping-Huan Kuo

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 10 citations into it
• Model Architectures — 7 citations into it
• Electrical and Electronic Engineering — 6 citations into it; #9,427 of 16,539 by trust here, #14,827 by citations
• Optimization & Training — 5 citations into it

Whom Ping-Huan Kuo trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Johan A. K. Suykens
2. Andy Liaw
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Sepp Hochreiter
6. Nitish Srivastava
7. Henrique S. Hippert
8. Ravinesh C. Deo
9. Ilya Sutskever
10. J. Urgen Schmidhuber

Who trusts Ping-Huan Kuo overall

1. Alberto Gasparin
2. Slobodan Lukovic
3. Taoying Li
4. Cesare Alippi

Papers from Ping-Huan Kuo's perspective

Ordered by how strongly the researchers Ping-Huan Kuo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Dropout: a simple way to prevent neural networks from overfitting

Their papers

• A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting (2018) — 296 citations
• A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities (2018) — 630 citations