在国际顶级会议KDD召开之际,在国际顶级会议KDD召开之际,来自阿里巴巴/微软/华为/Roku,以及上海交通大学/犹他大学等工业界/学术界资深同行,携手举办全球第二届面向高维稀疏数据的深度学习实践国际研讨会(The 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2021,简称DLP-KDD 2021),在此诚挚邀请学术界及工业界供稿。 DLP-KDD研讨会旨在深入、系统性地探讨深度学习在大规模工业级稀疏数据上的应用实践,论文内容包括但不限于以下主题。
征文主题
1. 深度学习系统搭建与优化 · Large Scale Recommendation and Retrieval System · High throughput and low latency real-time Serving System · Scalable, Distributed and Parallel Training System for Deep Learning · Auto Machine Learning · Auto feature selection 2. 数据表示与挖掘 · Representation Learning for High-dimensional Sparse Data · Embedding techniques · Manifold learning and dictionary learning · Applications of transfer learning · Meta learning for sparse data · Explainable deep learning for high dimensional data · Data augmentation · Anomaly Detection for High-dimensional Sparse data · Generative Adversarial Network for sparse data 3. 用户建模 · Large Scale User Response Prediction Modeling · User Behavior Understanding · User Interest Mining 当然,我们也欢迎其他主题的投稿,如深度学习在工业界生产实践中面临的具体挑战及探索经验。第三届面向高维稀疏数据的深度学习实践研讨会(DLP-KDD 2021),诚邀您的投稿与参与!