2019/06/13 by Timon Willi, Willi, Timon, Jonathan Masci +5 · 1 voice · 1 citation
Computer Science · Mathematics · #Artificial Intelligence in Games #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #Topic Modeling #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1906.05915
openalex publication_date 2019/06/13 · arxiv published 2019/06/13 · arxiv updated 2019/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We extend Neural Processes (NPs) to sequential data through Recurrent NPs or RNPs, a family of conditional state space models. RNPs model the state space with Neural Processes. Given time series observed on fast real-world time scales but containing slow long-term variabilities, RNPs may derive appropriate slow latent time scales. They do so in an efficient manner by establishing conditional independence among subsequences of the time series. Our theoretically grounded framework for stochastic processes expands the applicability of NPs while retaining their benefits of flexibility, uncertainty estimation, and favorable runtime with respect to Gaussian Processes (GPs). We demonstrate that state spaces learned by RNPs benefit predictive performance on real-world time-series data and nonlinear system identification, even in the case of limited data availability.