Yin Zheng

Computer Vision and Pattern Recognition · with Chen · 2014–2020

A researcher in the Science corpus: 8 papers; trusts 781 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 177 citations into it; #9,571 of 21,575 by trust here, #5,013 by citations
• Artificial Intelligence — 126 citations into it; #20,644 of 35,345 by trust here, #9,369 by citations
• Model Architectures — 51 citations into it
• Representation & Self-Supervised Learning — 31 citations into it; #3,040 of 3,145 by trust here, #2,355 by citations
• Optimization & Training — 25 citations into it
• Generative Models — 22 citations into it
• Probabilistic & Bayesian Methods — 22 citations into it
• Multimodal Learning — 13 citations into it

Whom Yin Zheng trusts overall

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

1. Geoffrey E. Hinton
2. Hugo Larochelle
3. Ruslan Salakhutdinov
4. Alex Krizhevsky
5. Yoshua Bengio
6. David M. Blei
7. Diederik P. Kingma
8. Kaiming He
9. Ilya Sutskever
10. Yann LeCun

Who trusts Yin Zheng overall

1. Yong Guo
2. Bangsheng Tang
3. Yusuke Sugano
4. Ting-Li Chen
5. Wonsung Lee
6. Kaiye Wang
7. John Arevalo
8. Harald Steck
9. Yongfeng Zhang
10. Xiangnan He

Papers from Yin Zheng's perspective

Ordered by how strongly the researchers Yin Zheng 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Dynamic Capacity Networks (2015) — 109 citations
• A Neural Autoregressive Approach to Attention-based Recognition (2015) — 30 citations
• Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering (2016) — 869 citations
• NAT: Neural Architecture Transformer for Accurate and Compact Architectures (2019) — 101 citations
• Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search (2020) — 78 citations
• A Neural Autoregressive Approach to Collaborative Filtering (2016) — 227 citations
• Topic Modeling of Multimodal Data: An Autoregressive Approach (2014) — 70 citations
• A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data (2014) — 63 citations