Yishu Miao

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 8 papers; trusts 251 researchers and is trusted by 89 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 133 citations into it; #3,858 of 21,575 by trust here, #5,161 by citations
• Artificial Intelligence — 132 citations into it; #2,956 of 35,345 by trust here, #6,014 by citations
• Natural Language Processing — 71 citations into it; #876 of 5,688 by trust here, #1,487 by citations
• Generative Models — 34 citations into it; #1,109 of 1,956 by trust here, #1,315 by citations
• Model Architectures — 32 citations into it
• Probabilistic & Bayesian Methods — 29 citations into it; #453 of 1,525 by trust here, #521 by citations
• Evaluation & Benchmarks — 25 citations into it
• Representation & Self-Supervised Learning — 25 citations into it

Whom Yishu Miao trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ruslan Salakhutdinov
5. Andriy Mnih
6. David M. Blei
7. Michael I. Jordan
8. Yann LeCun
9. Max Welling
10. Ilya Sutskever

Who trusts Yishu Miao overall

1. Dinghan Shen
2. Chunyuan Li
3. Adji Bousso Dieng
4. Pengcheng Yin
5. Tsung-Hsien Wen
6. Junxian He
7. Jianfeng Gao
8. Yau-Shian Wang
9. Tsendsuren Munkhdalai
10. W. Yu

Papers from Yishu Miao's perspective

Ordered by how strongly the researchers Yishu Miao 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Neural Variational Inference for Text Processing (2015) — 679 citations
• Kubric: A scalable dataset generator (2022) — 467 citations
• Language as a Latent Variable: Discrete Generative Models for Sentence Compression (2016) — 228 citations
• Latent Intention Dialogue Models (2017) — 148 citations
• Discovering Discrete Latent Topics with Neural Variational Inference (2017) — 344 citations
• Memory Architectures in Recurrent Neural Network Language Models (2018) — 66 citations
• Tag-Aware Personalized Recommendation Using a Hybrid Deep Model (2017) — 54 citations
• Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling (2016) — 85 citations