# Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

@article{Karl2017DeepVB, title={Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data}, author={Maximilian Karl and Maximilian S{\"o}lch and Justin Bayer and Patrick van der Smagt}, journal={ArXiv}, year={2017}, volume={abs/1605.06432} }

We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning of latent Markovian state space models. [... ] Key Result This also enables realistic long-term prediction. Expand

## 258 Citations

Linear Variational State Space Filtering

- Computer ScienceArXiv
- 2022

L-VSSF is introduced, a new method for unsupervised learning, identification, and filtering of latent Markov state space models from raw pixels with an explicit instantiation of this model with linear latent dynamics and Gaussian distribution parameterizations.

Recursive Variational Bayesian Dual Estimation for Nonlinear Dynamics and Non-Gaussian Observations

- Computer Science
- 2017

This work developed a flexible online learning framework for latent nonlinear state dynamics and filtered latent states using the stochastic gradient variational Bayes method to jointly optimize the parameters of the nonlinear dynamics, observation model, and the recognition model.

Latent Matters: Learning Deep State-Space Models

- Computer ScienceNeurIPS
- 2021

The extended Kalman VAE (EKVAE) is introduced, which combines amortised variational inference with classic Bayesian filtering/smoothing to model dynamics more accurately than RNN-based DSSMs.

Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces

- Computer ScienceICML
- 2019

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.

Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty

- Computer ScienceMechanical Systems and Signal Processing
- 2022

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

- Computer ScienceUAI
- 2021

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.

Self-Supervised Hybrid Inference in State-Space Models

- Computer ScienceArXiv
- 2021

Despite the model’s simplicity, it obtains competitive results on the chaotic Lorenz system compared to a fully supervised approach and outperform a method based on variational inference.

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction

- Computer Science2020 International Joint Conference on Neural Networks (IJCNN)
- 2020

The Recurrent Neural Filter (RNF), a novel recurrent autoencoder architecture that learns distinct representations for each Bayesian filtering step, captured by a series of encoders and decoders is introduced.

Variational Structured Stochastic Network

- Computer Science
- 2017

Variational Structured Stochastic Network (VSSN), a new method for modeling high dimensional structured data that can overcome intractable inference distributions via stochastic variational inference, is introduced.

Iterative Inference Models

- Computer Science
- 2017

This work proposes iterative inference models, which learn how to optimize a variational lower bound through repeatedly encoding gradients, and demonstrates the inference optimization capabilities of these models and shows that they outperform standard inference models on typical benchmark data sets.

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