no code implementations • 23 Oct 2023 • Xiaotian Han, Kaixiong Zhou, Ting-Hsiang Wang, Jundong Li, Fei Wang, Na Zou
Specifically, we first analyzed multiple graphs and observed that marginal nodes in graphs have a worse performance of downstream tasks than others in graph neural networks.
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.
no code implementations • NeurIPS 2020 • Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu
Motivated by the observation, in this paper, we propose to investigate the interaction detection problem from a novel topological perspective by analyzing the connectivity in neural networks.
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 • 28 Aug 2018 • Kwei-Herng Lai, Ting-Hsiang Wang, Heng-Yu Chi, Yi-An Chen, Ming-Feng Tsai, Chuan-Ju Wang
Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains.