Search Results for author: Mingjian Chen

Found 9 papers, 4 papers with code

AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020

1 code implementation25 Nov 2021 Jin Xu, Mingjian Chen, Jianqiang Huang, Xingyuan Tang, Ke Hu, Jian Li, Jia Cheng, Jun Lei

Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications.

Graph Classification Node Classification

AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data

1 code implementation9 Sep 2021 Zhipeng Luo, Zhixing He, Jin Wang, Manqing Dong, Jianqiang Huang, Mingjian Chen, Bohang Zheng

Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giving precise model predictions.

AutoML Feature Engineering

Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation

no code implementations1 Jun 2021 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.

Pose Estimation Quantization

AdaSpeech: Adaptive Text to Speech for Custom Voice

2 code implementations ICLR 2021 Mingjian Chen, Xu Tan, Bohan Li, Yanqing Liu, Tao Qin, Sheng Zhao, Tie-Yan Liu

2) To better trade off the adaptation parameters and voice quality, we introduce conditional layer normalization in the mel-spectrogram decoder of AdaSpeech, and fine-tune this part in addition to speaker embedding for adaptation.

MultiSpeech: Multi-Speaker Text to Speech with Transformer

1 code implementation8 Jun 2020 Mingjian Chen, Xu Tan, Yi Ren, Jin Xu, Hao Sun, Sheng Zhao, Tao Qin, Tie-Yan Liu

Transformer-based text to speech (TTS) model (e. g., Transformer TTS~\cite{li2019neural}, FastSpeech~\cite{ren2019fastspeech}) has shown the advantages of training and inference efficiency over RNN-based model (e. g., Tacotron~\cite{shen2018natural}) due to its parallel computation in training and/or inference.

Hybrid Channel Based Pedestrian Detection

no code implementations28 Dec 2019 Fiseha B. Tesema, Hong Wu, Mingjian Chen, Junpeng Lin, William Zhu, Kai-Zhu Huang

When using a more advanced RPN in our framework, our approach can be further improved and get competitive results on both benchmarks.

Pedestrian Detection

Adversarial Attack Type I: Cheat Classifiers by Significant Changes

no code implementations3 Sep 2018 Sanli Tang, Xiaolin Huang, Mingjian Chen, Chengjin Sun, Jie Yang

Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations.

Adversarial Attack

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