Zhaoyang Lv
Computer Vision and Pattern Recognition · 2015–2021
A researcher in the Science corpus: 5 papers; trusts 502 researchers and is trusted by 57 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 107 citations into it; #4,125 of 21,575 by trust here, #9,722 by citations
- Artificial Intelligence — 65 citations into it; #5,671 of 35,345 by trust here, #13,353 by citations
- Representation & Self-Supervised Learning — 39 citations into it; #1,086 of 3,145 by trust here, #2,066 by citations
- Statistics and Probability — 14 citations into it
- Media Technology — 13 citations into it
- Optimization & Training — 13 citations into it
- Model Architectures — 12 citations into it
- Computational Theory and Mathematics — 6 citations into it
Whom Zhaoyang Lv trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
- Geoffrey E. Hinton
- Karen Simonyan
- Diederik P. Kingma
- Yoshua Bengio
- Irfan Kaymaz
- Ross B. Girshick
- Oriol Vinyals
Who trusts Zhaoyang Lv overall
- Kai Han
- Zhun Zhong
- K. J. Joseph
- Bingchen Zhao
- Andrea Vedaldi
- Enrico Fini
- Subhankar Roy
- Juho Lee
- Gabriel Huang
- Andrew Zisserman
Papers from Zhaoyang Lv's perspective
Ordered by how strongly the researchers Zhaoyang Lv trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Learning to cluster in order to Transfer across domains and tasks (2017) — 246 citations
- Deep Image Category Discovery using a Transferred Similarity Function (2016) — 15 citations
- Multi-class Classification without Multi-class Labels (2019) — 200 citations
- A new learning function for Kriging and its applications to solve reliability problems in engineering (2015) — 379 citations
- Neural 3D Video Synthesis from Multi-view Video (2021) — 473 citations