Yunchen Pu

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 16 papers; trusts 1,400 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 376 citations into it; #6,217 of 21,575 by trust here, #2,966 by citations
• Artificial Intelligence — 353 citations into it; #9,035 of 35,345 by trust here, #4,923 by citations
• Natural Language Processing — 163 citations into it
• Generative Models — 151 citations into it; #725 of 1,956 by trust here, #367 by citations
• Model Architectures — 72 citations into it
• Probabilistic & Bayesian Methods — 66 citations into it; #1,159 of 1,525 by trust here, #832 by citations
• Optimization & Training — 63 citations into it
• Representation & Self-Supervised Learning — 60 citations into it; #1,834 of 3,145 by trust here, #2,120 by citations

Whom Yunchen Pu trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Yoshua Bengio
6. Kaiming He
7. Yann LeCun
8. Ruslan Salakhutdinov
9. Karen Simonyan
10. Jimmy Ba

Who trusts Yunchen Pu overall

1. Chunyuan Li
2. Fei Ye
3. Zhe Gan
4. Yizhe Zhang
5. Adrian G. Bors
6. Xiaopeng Li
7. Tian Han
8. Yibo Yang
9. Yao Qin
10. Lawrence Carin

Papers from Yunchen Pu's perspective

Ordered by how strongly the researchers Yunchen Pu 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Variational Autoencoder for Deep Learning of Images, Labels and Captions (2016) — 851 citations
• Semantic Compositional Networks for Visual Captioning (2016) — 451 citations
• Symmetric Variational Autoencoder and Connections to Adversarial Learning (2017) — 73 citations
• Triangle Generative Adversarial Networks (2017) — 142 citations
• Learning Generic Sentence Representations Using Convolutional Neural Networks (2016) — 101 citations
• Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling (2016) — 42 citations
• Adaptive Feature Abstraction for Translating Video to Language (2016) — 14 citations
• Adversarial Symmetric Variational Autoencoder (2017) — 81 citations
• Continuous-Time Flows for Efficient Inference and Density Estimation (2017) — 62 citations
• JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets (2018) — 44 citations
• Zero-Shot Learning via Class-Conditioned Deep Generative Models (2017) — 154 citations
• ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching (2017) — 227 citations
• Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification (2016) — 55 citations
• Adaptive Feature Abstraction for Translating Video to Text (2016) — 29 citations
• Unsupervised Learning of Sentence Representations using Convolutional Neural Networks (2016) — 20 citations
• A Deep Generative Deconvolutional Image Model (2015) — 45 citations