Chia-Hao Shen

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 72 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #17,614 of 35,345 by trust here, #14,181 by citations
• Representation & Self-Supervised Learning — 37 citations into it; #1,852 of 3,145 by trust here, #1,866 by citations
• Signal Processing — 37 citations into it; #3,582 of 7,572 by trust here, #2,184 by citations
• Speech & Audio — 35 citations into it; #563 of 1,076 by trust here, #587 by citations
• Natural Language Processing — 20 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Model Architectures — 13 citations into it
• Generative Models — 6 citations into it

Whom Chia-Hao Shen trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Alex Graves
5. J. Urgen Schmidhuber
6. Tomas Mikolov
7. Diederik P. Kingma
8. Alex Krizhevsky
9. Pascal Vincent
10. Yann LeCun

Who trusts Chia-Hao Shen overall

1. Yu-An Chung
2. Hung-yi Lee
3. Yu-Hsuan Wang
4. Kuan-Yu Chen
5. Yung-Sung Chuang

Papers from Chia-Hao Shen's perspective

Ordered by how strongly the researchers Chia-Hao Shen trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Audio Word2Vec: Unsupervised Learning of Audio Segment Representations Using Sequence-to-Sequence Autoencoder (2016) — 194 citations
• Audio Word2vec: Sequence-to-Sequence Autoencoding for Unsupervised Learning of Audio Segmentation and Representation (2019) — 38 citations
• Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data (2018) — 17 citations