Web: http://arxiv.org/abs/2107.13349

Jan. 26, 2022, 2:11 a.m. | David Ruhe, Patrick Forré

cs.LG updates on arXiv.org arxiv.org

We perform approximate inference in state-space models with nonlinear state
transitions. Without parameterizing a generative model, we apply Bayesian
update formulas using a local linearity approximation parameterized by neural
networks. This comes accompanied by a maximum likelihood objective that
requires no supervision via uncorrupt observations or ground truth latent
states. The optimization backpropagates through a recursion similar to the
classical Kalman filter and smoother. Additionally, using an approximate
conditional independence, we can perform smoothing without having to
parameterize a separate …

arxiv models space

More from arxiv.org / cs.LG updates on arXiv.org

Senior Data Engineer

@ DAZN | Hammersmith, London, United Kingdom

Sr. Data Engineer, Growth

@ Netflix | Remote, United States

Data Engineer - Remote

@ Craft | Wrocław, Lower Silesian Voivodeship, Poland

Manager, Operations Data Science

@ Binance.US | Vancouver

Senior Machine Learning Researcher for Copilot

@ GitHub | Remote - Europe

Sr. Marketing Data Analyst

@ HoneyBook | San Francisco, CA