# BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS

@article{Archer2016BLACKBV, title={BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS}, author={Evan Archer and Il Memming Park and Lars Buesing and John P. Cunningham and Liam Paninski}, journal={arXiv: Machine Learning}, year={2016} }

Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure both from noisy observations and from the temporal ordering in the data, where it is assumed that meaningful correlation structure exists across time. A few highly-structured models, such as the linear dynamical system with linear-Gaussian observations, have closed-form inference procedures (e.g. the Kalman Filter…

## 133 Citations

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This work proposes a new deep approach to Kalman filtering which can be learned directly in an end-to-end manner using backpropagation without additional approximations and uses a high-dimensional factorized latent state representation for which the Kalman updates simplify to scalar operations and thus avoids hard to backpropagate, computationally heavy and potentially unstable matrix inversions.

Structured Inference Networks for Nonlinear State Space Models

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A unified algorithm is introduced to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks.

The neural moving average model for scalable variational inference of state space models

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This work proposes an extension to state space models of time series data based on a novel generative model for latent temporal states: the neural moving average model, which permits a subsequence to be sampled without drawing from the entire distribution, enabling training iterations to use mini-batches of the time series at low computational cost.

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CBF-SSM is proposed a scalable model that employs a structured variational approximation to maintain temporal correlations in Gaussian processes and can be combined with physical models in the form of ordinary differential equations to learn a reliable model of a physical flying robotic vehicle.

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A new variational inference algorithm for learning in Gaussian Process State-Space Models (GPSSMs) with main algorithmic contribution a novel approximate posterior that can be calculated using a single forward and backward pass along the training trajec-tories.

Scalable approximate inference for state space models with normalising flows

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A novel generative model is introduced as a variational approximation, a local inverse autoregressive flow that allows a subsequence to be sampled without sampling the entire distribution, so that one can perform training iterations using short portions of the time series at low computational cost.

Structured Inference for Recurrent Hidden Semi-markov Model

- Computer ScienceIJCAI
- 2018

A structured and stochastic sequential neural network (SSNN), which composes with a generative network and an inference network that aims to not only capture the long-term dependencies but also model the uncertainty of the segmentation labels via semi-Markov models.

Stochastic Gradient MCMC for State Space Models

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This work proposes stochastic gradient estimators that control this bias by performing additional computation in a `buffer' to reduce breaking dependencies and develops novel SGMCMC samplers for discrete, continuous and mixed-type SSMs with analytic message passing.

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