Yuting Zhang

Computer Vision and Pattern Recognition · with Lee · 2014–2018

A researcher in the Science corpus: 7 papers; trusts 863 researchers and is trusted by 357 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 197 citations into it; #2,214 of 21,575 by trust here, #4,193 by citations
• Artificial Intelligence — 103 citations into it; #22,132 of 35,345 by trust here, #11,072 by citations
• Representation & Self-Supervised Learning — 56 citations into it; #1,452 of 3,145 by trust here, #1,395 by citations
• Model Architectures — 29 citations into it
• Generative Models — 23 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Signal Processing — 16 citations into it

Whom Yuting Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Kaiming He
5. Yann LeCun
6. Christian Szegedy
7. Diederik P. Kingma
8. David G. Lowe
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Yuting Zhang overall

1. Karl Ridgeway
2. Francesco Locatello
3. Aliaksandr Siarohin
4. Chelsea Finn
5. Gong Cheng
6. Scott E. Reed
7. Yoshua Bengio
8. Yanming Guo
9. Dan Hendrycks
10. Fan Yang

Papers from Yuting Zhang's perspective

Ordered by how strongly the researchers Yuting Zhang 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Deep Visual Analogy-Making (2015) — 344 citations
• Unsupervised Discovery of Object Landmarks as Structural Representations (2018) — 209 citations
• Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction (2015) — 221 citations
• Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification (2016) — 145 citations
• Towards Understanding the Invertibility of Convolutional Neural Networks (2017) — 67 citations
• Hierarchical Novelty Detection for Visual Object Recognition (2018) — 79 citations
• Learning to Disentangle Factors of Variation with Manifold Interaction (2014) — 254 citations