79 citations · 169 across the 9 of their papers we have counts for
9 papers
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
Jin Xu, Mingjian Chen, Jianqiang Huang +5
Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications. However, extensive manual work and domain knowledge…
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data
Zhipeng Luo, Zhixing He, Jin Wang +4
Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giv…
Deep Position-wise Interaction Network for CTR Prediction
Jianqiang Huang, Ke Hu, Qingtao Tang +4
Click-through rate (CTR) prediction plays an important role in online advertising and recommender systems. In practice, the training of CTR models depends on click data which is in…
Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation
Jie Ou, Mingjian Chen, Hong Wu
To achieve more accurate 2D human pose estimation, we extend the successful encoder-decoder network, simple baseline network (SBN), in three ways. To reduce the quantization errors…
AdaSpeech: Adaptive Text to Speech for Custom Voice
Mingjian Chen, Xu Tan, Bohan Li +4
Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims to adapt a source TTS model to synthesize personal voice for a target speaker using few s…
MultiSpeech: Multi-Speaker Text to Speech with Transformer
Mingjian Chen, Xu Tan, Yi Ren +5
Transformer-based text to speech (TTS) model (e.g., Transformer TTS~\cite{li2019neural}, FastSpeech~\cite{ren2019fastspeech}) has shown the advantages of training and inference eff…