Xinyi Zhou
Artificial Intelligence · with Du · 2017–2018
A researcher in the Science corpus: 2 papers; trusts 105 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 37 citations into it
- Information Systems — 37 citations into it; #5,714 of 8,771 by trust here, #7,386 by citations
- Sociology and Political Science — 33 citations into it; #14,018 of 22,756 by trust here, #18,468 by citations
- Computer Vision and Pattern Recognition — 13 citations into it; #19,310 of 21,575 by trust here, #18,782 by citations
- Statistical and Nonlinear Physics — 13 citations into it
- Natural Language Processing — 10 citations into it
- Computer Networks and Communications — 8 citations into it
- Signal Processing — 8 citations into it; #5,344 of 7,572 by trust here, #6,355 by citations
Whom Xinyi Zhou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Geoffrey E. Hinton
- Zhiwei Jin
- Alex Krizhevsky
- Yann LeCun
- Kai Shu
- Yoshua Bengio
- Karen Simonyan
- Shaoqing Ren
Who trusts Xinyi Zhou overall
- Rohit Kumar Kaliyar
- Liquan Zhao
- Khaled Alomar
- Pratik Narang
- Shrey Srivast
- Oludare Isaac Abiodun
- Tal Schuster
- Shuaiyang Li
- Halil Ibrahim Aysel
Papers from Xinyi Zhou's perspective
Ordered by how strongly the researchers Xinyi Zhou 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Fake News: A Survey of Research, Detection Methods, and Opportunities (2018) — 314 citations
- Application of deep learning in object detection (2017) — 355 citations