Xiaodong Gu

Artificial Intelligence · with Kim · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 133 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #22,071 of 35,345 by trust here, #14,181 by citations
• Natural Language Processing — 52 citations into it; #3,563 of 5,688 by trust here, #4,449 by citations
• Information Systems — 33 citations into it; #924 of 8,771 by trust here, #1,307 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Software — 13 citations into it
• Code Generation & Program Synthesis — 10 citations into it; #45 of 220 by trust here, #94 by citations
• Representation & Self-Supervised Learning — 10 citations into it
• Computer Science Applications — 7 citations into it

Whom Xiaodong Gu trusts overall

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

1. Ilya Sutskever
2. Yoshua Bengio
3. Tomas Mikolov
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Kyunghyun Cho
7. L. Eon Bottou
8. Alex Krizhevsky
9. Oriol Vinyals
10. Dzmitry Bahdanau

Who trusts Xiaodong Gu overall

1. Jian Zhang
2. Ilia Kulikov
3. Michele Tufano
4. Xi Victoria Lin
5. Xu Wang
6. Carl Yang
7. Hongyu Zhang
8. Siyuan Jiang
9. Cody Watson
10. Frank F. Xu

Papers from Xiaodong Gu's perspective

Ordered by how strongly the researchers Xiaodong Gu 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. 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep API learning (2016) — 579 citations
• DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder (2018) — 134 citations
• Deep Code Search (2018) — 687 citations