Jane Hung

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #21,138 of 35,345 by trust here, #5,880 by citations
• Natural Language Processing — 36 citations into it; #3,852 of 5,688 by trust here, #1,399 by citations
• Computer Vision and Pattern Recognition — 21 citations into it; #10,203 of 21,575 by trust here, #3,909 by citations
• Generative Models — 14 citations into it; #1,040 of 1,956 by trust here, #1,502 by citations
• Probabilistic & Bayesian Methods — 7 citations into it; #977 of 1,525 by trust here, #991 by citations

Whom Jane Hung trusts overall

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

1. Alec Radford
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Ronald J. Williams
7. Ilya Sutskever
8. Yann LeCun
9. Kaiming He
10. Ian J. Goodfellow

Who trusts Jane Hung overall

1. Michael Arbel
2. Akinori Tanaka
3. Jiaming Song
4. Sachin Kumar
5. Emily Sheng
6. Zhisheng Xiao
7. Abdul Fatir Ansari
8. Sangwoo Mo
9. Liang Zhou
10. Terrance Devries

Papers from Jane Hung's perspective

Ordered by how strongly the researchers Jane Hung 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Plug and Play Language Models: A Simple Approach to Controlled Text Generation (2019) — 1,229 citations
• Metropolis-Hastings Generative Adversarial Networks (2018) — 109 citations