Jiaming Xu

Artificial Intelligence · with Xu · 2015–2020

A researcher in the Science corpus: 4 papers; trusts 71 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it
• Natural Language Processing — 73 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Signal Processing — 24 citations into it
• Model Architectures — 22 citations into it
• Optimization & Training — 5 citations into it

Whom Jiaming Xu trusts overall

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

1. Tomas Mikolov
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Richard Socher
5. Ilya Sutskever
6. Alex Krizhevsky
7. Yann LeCun
8. Diederik P. Kingma
9. Kyunghyun Cho
10. Alex Graves

Who trusts Jiaming Xu overall

1. Tongshuang Sherry Wu

Papers from Jiaming Xu's perspective

Ordered by how strongly the researchers Jiaming Xu 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling (2016) — 528 citations
• Semantic Clustering and Convolutional Neural Network for Short Text Categorization (2015) — 184 citations
• Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals (2020) — 27 citations
• Self-Taught Convolutional Neural Networks for Short Text Clustering (2017) — 255 citations