Wei-Cheng Chang

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 213 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #15,760 of 35,345 by trust here, #11,490 by citations
• Natural Language Processing — 65 citations into it
• Computer Vision and Pattern Recognition — 63 citations into it; #10,750 of 21,575 by trust here, #5,711 by citations
• Generative Models — 25 citations into it; #995 of 1,956 by trust here, #639 by citations
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Information Retrieval & Search — 11 citations into it; #625 of 1,022 by trust here, #448 by citations

Whom Wei-Cheng Chang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Ilya Sutskever
6. Yann LeCun
7. Ruslan Salakhutdinov
8. Kaiming He
9. Alex Graves
10. Dzmitry Bahdanau

Who trusts Wei-Cheng Chang overall

1. Luyu Gao
2. Gautier Izacard
3. Jamie Callan
4. Sheng-Chieh Lin
5. Shervin Minaee
6. Yuanzhi Li
7. Zhuyun Dai
8. Wenhan Xiong
9. Ilias Chalkidis
10. Weiwei Liu

Papers from Wei-Cheng Chang's perspective

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

Their papers

• MMD GAN: Towards Deeper Understanding of Moment Matching Network (2017) — 807 citations
• Pre-training Tasks for Embedding-based Large-scale Retrieval (2020) — 327 citations
• XL-Editor: Post-editing Sentences with XLNet (2019) — 11 citations
• Deep Learning for Extreme Multi-label Text Classification (2017) — 735 citations
• Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks (2017) — 2,865 citations