Bangsheng Tang

Computer Vision and Pattern Recognition · 2016

A researcher in the Science corpus: 2 papers; trusts 108 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 45 citations into it; #20,907 of 21,575 by trust here, #9,872 by citations
• Artificial Intelligence — 32 citations into it
• Generative Models — 17 citations into it
• Representation & Self-Supervised Learning — 11 citations into it; #3,038 of 3,145 by trust here, #2,355 by citations
• Probabilistic & Bayesian Methods — 9 citations into it
• Information Systems — 8 citations into it
• Recommender Systems — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Bangsheng Tang trusts overall

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

1. Ruslan Salakhutdinov
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Hugo Larochelle
5. Yoshua Bengio
6. Alex Krizhevsky
7. Yann LeCun
8. Kaiming He
9. Andriy Mnih
10. Radford M. Neal

Who trusts Bangsheng Tang overall

1. Ting-Li Chen
2. Wonsung Lee
3. Harald Steck
4. Yongfeng Zhang
5. Xiangnan He
6. Jason S. Hartford
7. Zeynep Batmaz
8. Soyeon Caren Han

Papers from Bangsheng Tang's perspective

Ordered by how strongly the researchers Bangsheng Tang 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering (2016) — 869 citations
• A Neural Autoregressive Approach to Collaborative Filtering (2016) — 227 citations