509 research outputs found
Stochastic Collapsed Variational Inference for Sequential Data
Stochastic variational inference for collapsed models has recently been
successfully applied to large scale topic modelling. In this paper, we propose
a stochastic collapsed variational inference algorithm in the sequential data
setting. Our algorithm is applicable to both finite hidden Markov models and
hierarchical Dirichlet process hidden Markov models, and to any datasets
generated by emission distributions in the exponential family. Our experiment
results on two discrete datasets show that our inference is both more efficient
and more accurate than its uncollapsed version, stochastic variational
inference.Comment: NIPS Workshop on Advances in Approximate Bayesian Inference, 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
Robust Inference for State-Space Models with Skewed Measurement Noise
Filtering and smoothing algorithms for linear discrete-time state-space
models with skewed and heavy-tailed measurement noise are presented. The
algorithms use a variational Bayes approximation of the posterior distribution
of models that have normal prior and skew-t-distributed measurement noise. The
proposed filter and smoother are compared with conventional low-complexity
alternatives in a simulated pseudorange positioning scenario. In the
simulations the proposed methods achieve better accuracy than the alternative
methods, the computational complexity of the filter being roughly 5 to 10 times
that of the Kalman filter.Comment: 5 pages, 7 figures. Accepted for publication in IEEE Signal
Processing Letter
- âŚ