Name
Abstract | ||
|---|---|---|
We propose an efficient inference method for switching nonlinear dynamical systems. The key idea is to learn an inference network which can be used as a proposal distribution for the continuous latent variables, while performing exact marginalization of the discrete latent variables. This allows us to use the reparameterization trick, and apply end-to-end training with stochastic gradient descent. We show that the proposed method can successfully segment time series data (including videos) into meaningful "regimes", by using the piece-wise nonlinear dynamics. |
Year | Venue | DocType |
|---|---|---|
2020 | ICML | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Dong | 1 | 22 | 2.24 |
| Seybold Bryan A. | 2 | 0 | 0.34 |
| Michael Kuperberg | 3 | 7589 | 529.66 |
| Hung Hai Bui | 4 | 1188 | 112.37 |