1 code implementation • ICCV 2021 • Zirui Liu, Haifeng Jin, Ting-Hsiang Wang, Kaixiong Zhou, Xia Hu
We validate in experiments that the relative gain from automated data augmentation in test accuracy is highly correlated to Variance Diversity.
1 code implementation • 26 Jun 2020 • Ting-Hsiang Wang, Qingquan Song, Xiaotian Han, Zirui Liu, Haifeng Jin, Xia Hu
To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models.
no code implementations • 19 Jun 2020 • Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, Xia Hu
Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance.
no code implementations • 2 Jan 2019 • Qingquan Song, Haifeng Jin, Xiao Huang, Xia Hu
Experiments on real-world multi-label image classification and ranking problems demonstrate the effectiveness of our proposed frameworks and provide insights of the vulnerability of multi-label deep learning models under diverse targeted attacking strategies.
no code implementations • 2018 IEEE International Conference on Big Knowledge (ICBK) 2018 • Haifeng Jin, Qingquan Song, Xia Hu
Moreover, the learned vector representations are not in a smooth space since the values can only be integers.
Ranked #12 on Graph Classification on PTC
14 code implementations • 27 Jun 2018 • Haifeng Jin, Qingquan Song, Xia Hu
In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.