Ju-Chieh Chou

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 74 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #19,796 of 35,345 by trust here, #15,866 by citations
• Signal Processing — 67 citations into it; #4,127 of 7,572 by trust here, #2,816 by citations
• Speech & Audio — 56 citations into it; #634 of 1,076 by trust here, #654 by citations
• Natural Language Processing — 29 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Model Architectures — 17 citations into it
• Generative Models — 11 citations into it

Whom Ju-Chieh Chou trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Diederik P. Kingma
5. Ilya Sutskever
6. Individualized Treat
7. Alex Krizhevsky
8. Kaiming He
9. Wei-Ning Hsu
10. Yann LeCun

Who trusts Ju-Chieh Chou overall

1. Po-Chun Hsu
2. Andy T. Liu
3. Ali Asghar Zare
4. Kaizhi Qian
5. Hung-yi Lee
6. Wei-Ning Hsu
7. Tzu-hsien Huang
8. Jordi Pons
9. Momina Masood
10. Nima Mesgarani

Papers from Ju-Chieh Chou's perspective

Ordered by how strongly the researchers Ju-Chieh Chou 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Layer-Wise Analysis of a Self-Supervised Speech Representation Model (2021) — 472 citations
• SpeechBrain: A General-Purpose Speech Toolkit (2021) — 999 citations
• Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations (2018) — 137 citations