Wen-Chin Huang
Artificial Intelligence · 2019–2022
A researcher in the Science corpus: 6 papers; trusts 71 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 148 citations into it
- Signal Processing — 105 citations into it
- Speech & Audio — 77 citations into it
- Natural Language Processing — 52 citations into it
- Computer Vision and Pattern Recognition — 48 citations into it
- Representation & Self-Supervised Learning — 24 citations into it
- Generative Models — 22 citations into it
- Optimization & Training — 6 citations into it
Whom Wen-Chin Huang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Aäron van den Oord
- Jimmy Ba
- Oriol Vinyals
- Ilya Sutskever
- Alex Graves
- Dzmitry Bahdanau
- Alex Krizhevsky
Who trusts Wen-Chin Huang overall
Papers from Wen-Chin Huang's perspective
Ordered by how strongly the researchers Wen-Chin Huang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Many-to-Many Voice Transformer Network (2020) — 33 citations
- SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities (2022) — 133 citations
- Pretraining Techniques for Sequence-to-Sequence Voice Conversion (2020) — 49 citations
- End-to-End Binaural Speech Synthesis (2022) — 15 citations
- The AS-NU System for the M2VoC Challenge (2021)
- MOSNet: Deep Learning based Objective Assessment for Voice Conversion (2019) — 373 citations