Yijun Huang
Artificial Intelligence · 2015–2016
A researcher in the Science corpus: 2 papers; trusts 43 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 48 citations into it; #10,291 of 35,345 by trust here, #8,525 by citations
- Optimization & Training — 37 citations into it; #1,044 of 2,601 by trust here, #804 by citations
- Computational Mechanics — 29 citations into it; #3,151 of 10,001 by trust here, #1,409 by citations
- Computer Networks and Communications — 13 citations into it
- Management Science and Operations Research — 9 citations into it
- Computational Theory and Mathematics — 8 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
- Statistics and Probability — 7 citations into it
Whom Yijun Huang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Mingyi Hong
- Alex Krizhevsky
- Stephen J. Wright
- Yurii E. Nesterov
- Dimitri P. Bertsekas
- Geoffrey E. Hinton
- Hamid Reza Feyzmahdavian
- Yann LeCun
- Peter Richtárik
Who trusts Yijun Huang overall
- Xiangru Lian
- Dan Alistarh
- Hanlin Tang
- Wei Zhang
- Ce Zhang
- Suyog Gupta
- Jianyu Wang
- Sebastian U. Stich
- Ji Liu
- Soham De
Papers from Yijun Huang's perspective
Ordered by how strongly the researchers Yijun Huang 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)
- Learning Multiple Layers of Features from Tiny Images
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)