Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement LearningEI
Learning node representations for networks has attracted much attention recently due to its effectiveness in a variety of applications. This paper focuses on learning node representations for heterogeneous star networks, which have a center node type linked with multiple attribute node types through different types of edges. In heterogeneous star networks, we observe that the training order of different types of edges affects the learning performance significantly. Therefore we study learning curricula for node representation learning...更多
- 2Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks.Annual Conference on Neural Information Processing Systems, 2015.
- 7Sylvain Gelly, David Silver. Combining online and offline knowledge in UCT., 2007.
- 11David Silver, Richard S. Sutton, Martin Müller. Sample-based learning and search with permanent and transient memories.ICML, pp. 968-975, 2008.
- 13Jian Tang, Meng Qu, Qiaozhu Mei. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks.ACM Knowledge Discovery and Data Mining, 2015.
- 16Jeffrey L. Elman, Learning and development in neural networks: the importance of starting small.Cognition, pp. 71-99, 1993.
- 18Bryan Perozzi, Rami Al-Rfou', Steven Skiena. DeepWalk: online learning of social representations.KDD, pp. 701-710, 2014.
- 19Yong Jae Lee, K. Grauman. Learning the easy things first: Self-paced visual category discovery.CVPR, pp. 1721-1728, 2011.
- 20Jérôme Louradour, Christopher Kermorvant. Curriculum Learning for Handwritten Text Line Recognition.Document Analysis Systems, pp. 56-60, 2014.
- 21Peter Auer, Nicolò Cesa-Bianchi, Paul Fischer. Finite-time Analysis of the Multiarmed Bandit Problem.Machine Learning, pp. 235-256, 2002.
- 22Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei. Visualizing Large-scale and High-dimensional Data.WWW, pp. 287-297, 2016.
- 23Silver David, Huang Aja, Maddison Chris J, Guez Arthur, Sifre Laurent, van den Driessche George, Schrittwieser Julian, Antonoglou Ioannis, Panneershelvam Veda, Lanctot Marc, Dieleman Sander, Grewe Dominik, Nham John, Kalchbrenner Nal, Sutskever Ilya, Lillicrap Timothy, Leach Madeleine, Kavukcuoglu Koray, Graepel Thore, Hassabis Demis. Mastering the game of Go with deep neural networks and tree search.Nature, pp. 484-489, 2016.
WSDM, pp. 468-476, 2018.