Zhiguang Wang

Artificial Intelligence · with Oates · 2014–2016

A researcher in the Science corpus: 3 papers; trusts 129 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #8,721 of 35,345 by trust here, #16,471 by citations
• Signal Processing — 23 citations into it; #1,471 of 7,572 by trust here, #2,664 by citations
• Computer Vision and Pattern Recognition — 19 citations into it
• Model Architectures — 11 citations into it
• Economics and Econometrics — 9 citations into it
• Optimization & Training — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Zhiguang Wang trusts overall

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

1. Geoffrey E. Hinton
2. Eamonn J. Keogh
3. Alex Krizhevsky
4. Yoshua Bengio
5. Hynek Hermansky
6. Yann LeCun
7. Diederik P. Kingma
8. Ilya Sutskever
9. Kaiming He
10. Quoc V. Le

Who trusts Zhiguang Wang overall

1. Hassan Ismail Fawaz
2. John Cristian Borges Gamboa
3. Fazle Karim
4. Rahul Katarya
5. Marc Rußwurm
6. Brian Kenji Iwana
7. Charlotte Pelletier
8. Germain Forestier
9. Jean-Yves Franceschi
10. Somshubra Majumdar

Papers from Zhiguang Wang's perspective

Ordered by how strongly the researchers Zhiguang Wang trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Time series classification from scratch with deep neural networks: A strong baseline (2016) — 2,081 citations
• Imaging Time-Series to Improve Classification and Imputation (2015) — 928 citations
• Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks (2014) — 636 citations