Yuping Wang
Artificial Intelligence · with Wang · 2020–2024
A researcher in the Science corpus: 5 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 133 citations into it
- Signal Processing — 86 citations into it
- Speech & Audio — 78 citations into it
- Computer Vision and Pattern Recognition — 53 citations into it
Whom Yuping Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- Yoshua Bengio
- Yuxuan Wang
- Alex Krizhevsky
- Ilya Sutskever
- Alex Graves
- Yann LeCun
- Jean-Marc Valin
Papers from Yuping Wang's perspective
Ordered by how strongly the researchers Yuping Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- PolyVoice: Language Models for Speech to Speech Translation (2023) — 35 citations
- Seed-TTS: A Family of High-Quality Versatile Speech Generation Models (2024) — 395 citations
- AudioLDM 2: Learning Holistic Audio Generation With Self-Supervised Pretraining (2023) — 512 citations
- LM-VC: Zero-Shot Voice Conversion via Speech Generation Based on Language Models (2023) — 47 citations
- Noise Robust TTS for Low Resource Speakers using Pre-trained Model and Speech Enhancement (2020) — 7 citations