XDL

作者：Biye Jiang, Chao Deng, Huimin Yi, Zelin Hu, Guorui Zhou, Zheng Yang, Sui Huang, Xinyang Guo, Dongyue Wang, Yue Song, Liqin Zhao, Zhi Wang, Peng Sun, Yu Zhang, Di Zhang, Jinhui Li, Jian Xu, Xiaoqiang Zhu, Kun Gai · 年份：2019 · DOI：10.1145/3326937.3341255 · 被引用次数：54 · 研究领域：Web Data Mining and Analysis、Topic Modeling、Advanced Neural Network Applications

With the rapid growth of data and computing power, deep learning based approaches have become the main solution for many artificial intelligence problems such as image classification, speech recognition and computer vision. Several excellent deep learning (DL) frameworks including Tensorflow, MxNet and PyTorch have been made open-sourced, further accelerating the advance of the community. However, existing DL frameworks are not designed for applications involving high-dimensional sparse data, which exists widely in many successful online businesses such as search engine, recommender systems and online advertising. In these industrial scenarios, deep models are typically trained on large scale datasets with up to billions of sparse features and hundreds of billions of samples, bringing great challenges to DL framework.