Alexander H. Liu

Artificial Intelligence · with Lee · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 162 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it; #11,769 of 35,345 by trust here, #13,962 by citations
• Speech & Audio — 54 citations into it; #467 of 1,076 by trust here, #613 by citations
• Signal Processing — 51 citations into it; #4,769 of 7,572 by trust here, #5,086 by citations
• Natural Language Processing — 31 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it; #18,045 of 21,575 by trust here, #14,822 by citations
• Representation & Self-Supervised Learning — 30 citations into it; #2,596 of 3,145 by trust here, #2,757 by citations
• Generative Models — 26 citations into it; #1,644 of 1,956 by trust here, #1,549 by citations
• Model Architectures — 11 citations into it

Whom Alexander H. Liu trusts overall

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

1. Alex Graves
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Aäron van den Oord
6. Alex Krizhevsky
7. Oriol Vinyals
8. Ilya Sutskever
9. Yann LeCun
10. Kaiming He

Who trusts Alexander H. Liu overall

1. Xingchao Peng
2. Murali Karthick Baskar
3. Xu Tan
4. Yu-An Chung
5. Alexei Baevski
6. Yang Chen
7. Weihao Xia
8. Tao Qin
9. Mutian He
10. Shinji Watanabe

Papers from Alexander H. Liu's perspective

Ordered by how strongly the researchers Alexander H. Liu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation (2018) — 232 citations
• Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model (2018) — 47 citations
• Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning (2019) — 53 citations
• Non-Autoregressive Predictive Coding for Learning Speech Representations from Local Dependencies (2020) — 96 citations
• Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation (2020) — 8 citations