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Deep Variational Bayes Filters (2017)

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"Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data",
Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt, ICLR2017.

[Link] https://arxiv.org/abs/1605.06432

Published in: Engineering
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Deep Variational Bayes Filters (2017)

  1. 1. Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry Taewoong Um DEEP VARIATIONAL BAYES FILTERS : UNSUPERVISED LEARNING OF STATE SPACE MODELS FROM RAW DATA 1
  2. 2. 2 WHAT’S WRONG WITH RNN (LSTM)? Terry Taewoong Um (terry.t.um@gmail.com) WHAT’S WRONG WITH AUTOENCODER?
  3. 3. 3 LIN. REGRE : BAYES. LIN. REGRE. = AE : VAE = RNN : VARIATIONAL RNN Terry Taewoong Um (terry.t.um@gmail.com) From “PR12: Variational Autoencoder” by Cha
  4. 4. 4 VAE / HMM / KF / RNN Terry Taewoong Um (terry.t.um@gmail.com) Latent space (q_0,…,q_N) Observation space (x,y,z) Latent t t+1 t+2emission transition Hidden Markov Model : discrete states, stochastic transition/emission Kalman filter : continuous states, stochastic linear transition/emission with Gaussians Recurrent Neural Networks : deterministic (not good for learning probabilistic densities) t+2 You should define the model a priori! X Z
  5. 5. 5 KALMAN FILTER Terry Taewoong Um (terry.t.um@gmail.com) Latent space (q_0,…,q_N) Observation space (x,y,z) Latent t t+1 t+2 emission transition t+2 X Z U [Limitations] (1) Its assumptions are restrictive (2) The model (F, B, H) has to be known
  6. 6. 6 BAYESIAN FILTER Terry Taewoong Um (terry.t.um@gmail.com) emission transition In Kalman filter,
  7. 7. 7 VAE AND VARIATIONAL RNN Terry Taewoong Um (terry.t.um@gmail.com) • “Structured Inference Networks for Nonlinear State Space Models”, R. Krishnan, U. Shalit, and D. Sontag, AAAI2017
  8. 8. 8 VAE AND VARIATIONAL RNN Terry Taewoong Um (terry.t.um@gmail.com) • “Deep Kalman Filter”, R. Krishnan, U. Shalit, and D. Sontag, NIPS2016
  9. 9. 9 REFERENCES Terry Taewoong Um (terry.t.um@gmail.com) • “Deep Kalman Filter”, R. Krishnan, U. Shalit, D. Sontag, NIPS2016 • “Structured Inference Networks for Nonlinear State Space Models”, R. Krishnan, U. Shalit, and D. Sontag, AAAI2017 • “Learning Stochastic Recurrent Networks”, J. Bayer, C. Osendorfer, ICLR2015 • “Recurrent Latent Variable Model for Sequential Data”, J. Chung, K. Kastner, L. Dinh, K. Goel, A. Courville, Y. Bengio, NIPS2015 • “Variational Bayes Filters”, M. Karl, M. Soelch, J. Bayer, P. Smagt, ICLR2017 • “A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning”, M. Fraccaroy, S. Kamronn, U. Paquetz, O. Winthery, NIPS2017
  10. 10. 10 RELATED WORKS Terry Taewoong Um (terry.t.um@gmail.com)
  11. 11. 11 VAE REVIEW “All about VAE”, H. Lee, https://www.slideshare.net/NaverEngineering/ss-96581209
  12. 12. 12 VAE REVIEW “All about VAE”, H. Lee, https://www.slideshare.net/NaverEngineering/ss-96581209
  13. 13. 13 REPARAMETERIZATION “Deep Variational Bayes Filters”, M. Karl et al., ICLR2017
  14. 14. 14 REPARAMETERIZATION “Deep Variational Bayes Filters”, M. Karl et al., ICLR2017
  15. 15. 15 VAE REVIEW Terry Taewoong Um (terry.t.um@gmail.com)
  16. 16. 16 VAE REVIEW Terry Taewoong Um (terry.t.um@gmail.com)
  17. 17. 17 BAYESIAN FILTER “Deep Variational Bayes Filters”, M. Karl et al., ICLR2017 emission transition Markov assumption
  18. 18. 18 ELBO “Deep Variational Bayes Filters”, M. Karl et al., ICLR2017 deterministic transition given 𝛽
  19. 19. 19 EXPERIMENTS Terry Taewoong Um (terry.t.um@gmail.com) http://blog.fastforwardlabs.com/2016/08/12/introdu cing-variational-autoencoders-in-prose-and.html
  20. 20. 20 EXPERIMENTS “Deep Variational Bayes Filters”, M. Karl et al., ICLR2017
  21. 21. 21 EXPERIMENTS “Deep Variational Bayes Filters”, M. Karl et al., ICLR2017
  22. 22. 22 DEEP HMM Terry Taewoong Um (terry.t.um@gmail.com) • “Structured Inference Networks for Nonlinear State Space Models”, AAAI2017
  23. 23. 23 KALMAN VAE “Deep Variational Bayes Filters”, M. Karl et al., ICLR2017 • “A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning”, M. Fraccaroy, S. Kamronn, U. Paquetz, O. Winthery, NIPS2017
  24. 24. 24 REFERENCES Terry Taewoong Um (terry.t.um@gmail.com) • “Deep Kalman Filter”, R. Krishnan, U. Shalit, D. Sontag, NIPS2016 • “Structured Inference Networks for Nonlinear State Space Models”, R. Krishnan, U. Shalit, and D. Sontag, AAAI2017 • “Learning Stochastic Recurrent Networks”, J. Bayer, C. Osendorfer, ICLR2015 • “Recurrent Latent Variable Model for Sequential Data”, J. Chung, K. Kastner, L. Dinh, K. Goel, A. Courville, Y. Bengio, NIPS2015 • “Variational Bayes Filters”, M. Karl, M. Soelch, J. Bayer, P. Smagt, ICLR2017 • “A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning”, M. Fraccaroy, S. Kamronn, U. Paquetz, O. Winthery, NIPS2017
  25. 25. 25 END Terry Taewoong Um (terry.t.um@gmail.com) Thank you

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