89 citations · 118 across the 4 of their papers we have counts for
4 papers
Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons
Wei Huang, Yuan Cao, Haonan Wang +2
Graph neural networks (GNNs) have demonstrated remarkable capabilities in learning from graph-structured data, often outperforming traditional Multilayer Perceptrons (MLPs) in nume…
Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis
Guangzhi Sun, Yu Zhang, Ron J. Weiss +3
This paper proposes a hierarchical, fine-grained and interpretable latent variable model for prosody based on the Tacotron 2 text-to-speech model. It achieves multi-resolution mode…
Alternating Multi-bit Quantization for Recurrent Neural Networks
Chen Xu, Jianqiang Yao, Zhouchen Lin +4
Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. F…
Local and Global Inference for High Dimensional Nonparanormal Graphical Models
Quanquan Gu, Yuan Cao, Yang Ning +1
This paper proposes a unified framework to quantify local and global inferential uncertainty for high dimensional nonparanormal graphical models. In particular, we consider the pro…