Da-Rong Liu

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 128 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 172 citations into it; #27,183 of 35,345 by trust here, #10,942 by citations
• Signal Processing — 113 citations into it; #5,797 of 7,572 by trust here, #3,566 by citations
• Speech & Audio — 101 citations into it; #715 of 1,076 by trust here, #470 by citations
• Natural Language Processing — 57 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Generative Models — 17 citations into it
• Model Architectures — 16 citations into it

Whom Da-Rong Liu trusts overall

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

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

Who trusts Da-Rong Liu overall

1. Xu Tan
2. Alexei Baevski
3. Raheel Qader
4. Takuma Okamoto
5. Fengpeng Yue
6. Chih-Kuan Yeh
7. Abdel-rahman Mohamed
8. Kuan-Yu Chen
9. Hung-yi Lee
10. Tao Qin

Papers from Da-Rong Liu's perspective

Ordered by how strongly the researchers Da-Rong 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings (2018) — 51 citations
• SUPERB: Speech processing Universal PERformance Benchmark (2021) — 1,225 citations
• SpeechNet: A Universal Modularized Model for Speech Processing Tasks (2021) — 19 citations
• Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech (2021) — 77 citations
• Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models (2019) — 19 citations
• Improving Unsupervised Style Transfer in end-to-end Speech Synthesis with end-to-end Speech Recognition (2018) — 18 citations