Haoze Sun

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it; #31,711 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 18 citations into it; #19,520 of 21,575 by trust here, #11,950 by citations
• Generative Models — 16 citations into it
• Natural Language Processing — 14 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Optimization & Training — 10 citations into it
• Model Architectures — 7 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it

Whom Haoze Sun trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Danilo Jimenez Rezende
5. Sergey Ioffe
6. Ronald J. Williams
7. Alex Krizhevsky
8. Max Welling
9. Sepp Hochreiter
10. Oriol Vinyals

Who trusts Haoze Sun overall

1. Iqbal H. Sarker
2. Babak Esmaeili
3. Jason Ramapuram
4. Hui Liu
5. Yu Meng
6. Xinting Huang
7. Dinghan Shen
8. Jiaao Chen

Papers from Haoze Sun's perspective

Ordered by how strongly the researchers Haoze Sun trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Variational Autoencoder for Semi-Supervised Text Classification (2017) — 236 citations
• Semi-supervised Variational Autoencoders for Sequence Classification (2016) — 7 citations