1,939 research outputs found
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
This paper takes a step towards temporal reasoning in a dynamically changing
video, not in the pixel space that constitutes its frames, but in a latent
space that describes the non-linear dynamics of the objects in its world. We
introduce the Kalman variational auto-encoder, a framework for unsupervised
learning of sequential data that disentangles two latent representations: an
object's representation, coming from a recognition model, and a latent state
describing its dynamics. As a result, the evolution of the world can be
imagined and missing data imputed, both without the need to generate high
dimensional frames at each time step. The model is trained end-to-end on videos
of a variety of simulated physical systems, and outperforms competing methods
in generative and missing data imputation tasks.Comment: NIPS 201
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation
The control of nonlinear dynamical systems remains a major challenge for
autonomous agents. Current trends in reinforcement learning (RL) focus on
complex representations of dynamics and policies, which have yielded impressive
results in solving a variety of hard control tasks. However, this new
sophistication and extremely over-parameterized models have come with the cost
of an overall reduction in our ability to interpret the resulting policies. In
this paper, we take inspiration from the control community and apply the
principles of hybrid switching systems in order to break down complex dynamics
into simpler components. We exploit the rich representational power of
probabilistic graphical models and derive an expectation-maximization (EM)
algorithm for learning a sequence model to capture the temporal structure of
the data and automatically decompose nonlinear dynamics into stochastic
switching linear dynamical systems. Moreover, we show how this framework of
switching models enables extracting hierarchies of Markovian and
auto-regressive locally linear controllers from nonlinear experts in an
imitation learning scenario.Comment: 2nd Annual Conference on Learning for Dynamics and Contro
Bayesian Conditional Cointegration
Cointegration is an important topic for time-series, and describes a
relationship between two series in which a linear combination is stationary.
Classically, the test for cointegration is based on a two stage process in
which first the linear relation between the series is estimated by Ordinary
Least Squares. Subsequently a unit root test is performed on the residuals. A
well-known deficiency of this classical approach is that it can lead to
erroneous conclusions about the presence of cointegration. As an alternative,
we present a framework for estimating whether cointegration exists using
Bayesian inference which is empirically superior to the classical approach.
Finally, we apply our technique to model segmented cointegration in which
cointegration may exist only for limited time. In contrast to previous
approaches our model makes no restriction on the number of possible
cointegration segments.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
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