Ontology highlight
ABSTRACT:
SUBMITTER: Al-Shedivat M
PROVIDER: S-EPMC6334642 | biostudies-other | 2017 Jan
REPOSITORIES: biostudies-other
Al-Shedivat Maruan M Wilson Andrew Gordon AG Saatchi Yunus Y Hu Zhiting Z Xing Eric P EP
Journal of machine learning research : JMLR 20170101 1
Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the inductive biases of long short-term memory (LSTM) recurrent networks, while retaining the non-parametric probabilistic a ...[more]