Zhicheng Cui
Artificial Intelligence · 2015–2019
A researcher in the Science corpus: 4 papers; trusts 110 researchers and is trusted by 62 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 79 citations into it; #4,257 of 35,345 by trust here, #6,664 by citations
- Computer Vision and Pattern Recognition — 50 citations into it; #6,068 of 21,575 by trust here, #6,606 by citations
- Graph Learning — 38 citations into it; #191 of 693 by trust here, #243 by citations
- Model Architectures — 17 citations into it
- Computational Theory and Mathematics — 15 citations into it
- Signal Processing — 15 citations into it; #968 of 7,572 by trust here, #3,428 by citations
- Information Systems — 9 citations into it; #1,331 of 8,771 by trust here, #1,246 by citations
- Molecular Biology — 8 citations into it
Whom Zhicheng Cui trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- John Paparrizos
- Yoshua Bengio
- Christian Szegedy
- Diederik P. Kingma
- Leo Breiman
- Stéphane Mallat
- Ilya Sutskever
- Jaka Demsar
Who trusts Zhicheng Cui overall
- Weiwei Jiang
- Angus Dempster
- Jean-Yves Franceschi
- Zhiguang Wang
- Arthur Le Guennec
- Nidhi Dua
- Aaqib Saeed
- Nils Strodthoff
- Håvard Kvamme
- Ihsan Ullah
Papers from Zhicheng Cui's perspective
Ordered by how strongly the researchers Zhicheng Cui 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- An End-to-End Deep Learning Architecture for Graph Classification (2018) — 1,721 citations
- D-VAE: A Variational Autoencoder for Directed Acyclic Graphs (2019) — 238 citations
- Multi-Scale Convolutional Neural Networks for Time Series Classification (2016) — 622 citations
- Optimal Action Extraction for Random Forests and Boosted Trees (2015) — 129 citations