Wei-Tsung Kao

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Natural Language Processing — 18 citations into it
• Signal Processing — 13 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Speech & Audio — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Wei-Tsung Kao trusts overall

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

1. Geoffrey E. Hinton
2. Richard Socher
3. Jacob Devlin
4. Yoshua Bengio
5. Ashish Vaswani
6. Diederik P. Kingma
7. Ilya Sutskever
8. Alex Krizhevsky
9. Samuel R. Bowman
10. Pranav Rajpurkar

Papers from Wei-Tsung Kao's perspective

Ordered by how strongly the researchers Wei-Tsung Kao trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Utilizing Self-supervised Representations for MOS Prediction (2021) — 73 citations
• Further Boosting BERT-based Models by Duplicating Existing Layers: Some Intriguing Phenomena inside BERT (2020) — 5 citations