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  1. DATA AUGMENTATION FOR META-LEARNING

    NI, RENKUN / GOLDBLUM, MICAH / SHARAF, AMR et al. | TIBKAT | 2022

  2. Data Augmentation for Meta-Learning

    Free access
    Ni, Renkun / Goldblum, Micah / Sharaf, Amr et al. | ArXiv | 2020

  3. Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy

    Free access
    Chen, Chen / Kong, Kezhi / Yu, Peihong et al. | ArXiv | 2021

  4. GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training

    Free access
    Zhu, Chen / Ni, Renkun / Xu, Zheng et al. | ArXiv | 2021

  5. GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training

    Zhu, Chen / Ni, Renkun / Xu, Zheng et al. | TIBKAT | 2022

  6. SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations

    Free access
    Feng, Hao-Zhe / Kong, Kezhi / Chen, Minghao et al. | ArXiv | 2020

  7. A Visual Analytics Approach for Traffic Flow Prediction Ensembles

    Kong, Kezhi / Ma, Yuxin / Ye, Chentao et al. | Eurographics | 2018

  8. VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

    Ding, Mucong / Kong, Kezhi / Li, Jingling et al. | TIBKAT | 2022

  9. Exploring linear projections for revealing clusters, outliers, and trends in subsets of multi-dimensional datasets

    Xia, Jiazhi / Gao, Le / Kong, Kezhi et al. | British Library Online Contents | 2018

  10. A Visual Analytics Approach for Traffic Flow Prediction Ensembles

    Kong, Kezhi / Ma, Yuxin / Ye, Chentao et al. | DataCite | 2018

  11. VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

    Free access
    Ding, Mucong / Kong, Kezhi / Li, Jingling et al. | ArXiv | 2021

  12. Robust Optimization as Data Augmentation for Large-scale Graphs

    Free access
    Kong, Kezhi / Li, Guohao / Ding, Mucong et al. | ArXiv | 2020
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