51,711 research outputs found
A probabilistic data-driven model for planar pushing
This paper presents a data-driven approach to model planar pushing
interaction to predict both the most likely outcome of a push and its expected
variability. The learned models rely on a variation of Gaussian processes with
input-dependent noise called Variational Heteroscedastic Gaussian processes
(VHGP) that capture the mean and variance of a stochastic function. We show
that we can learn accurate models that outperform analytical models after less
than 100 samples and saturate in performance with less than 1000 samples. We
validate the results against a collected dataset of repeated trajectories, and
use the learned models to study questions such as the nature of the variability
in pushing, and the validity of the quasi-static assumption.Comment: 8 pages, 11 figures, ICRA 201
Computationally Efficient and Robust BIC-Based Speaker Segmentation
An algorithm for automatic speaker segmentation based on the Bayesian information criterion (BIC) is presented. BIC tests are not performed for every window shift, as previously, but when a speaker change is most probable to occur. This is done by estimating the next probable change point thanks to a model of utterance durations. It is found that the inverse Gaussian fits best the distribution of utterance durations. As a result, less BIC tests are needed, making the proposed system less computationally demanding in time and memory, and considerably more efficient with respect to missed speaker change points. A feature selection algorithm based on branch and bound search strategy is applied in order to identify the most efficient features for speaker segmentation. Furthermore, a new theoretical formulation of BIC is derived by applying centering and simultaneous diagonalization. This formulation is considerably more computationally efficient than the standard BIC, when the covariance matrices are estimated by other estimators than the usual maximum-likelihood ones. Two commonly used pairs of figures of merit are employed and their relationship is established. Computational efficiency is achieved through the speaker utterance modeling, whereas robustness is achieved by feature selection and application of BIC tests at appropriately selected time instants. Experimental results indicate that the proposed modifications yield a superior performance compared to existing approaches
A Manifesto for the Equifinality Thesis.
This essay discusses some of the issues involved in the identification and predictions of hydrological models given some calibration data. The reasons for the incompleteness of traditional calibration methods are discussed. The argument is made that the potential for multiple acceptable models as representations of hydrological and other environmental systems (the equifinality thesis) should be given more serious consideration than hitherto. It proposes some techniques for an extended GLUE methodology to make it more rigorous and outlines some of the research issues still to be resolved
Lipschitz Optimisation for Lipschitz Interpolation
Techniques known as Nonlinear Set Membership prediction, Kinky Inference or
Lipschitz Interpolation are fast and numerically robust approaches to
nonparametric machine learning that have been proposed to be utilised in the
context of system identification and learning-based control. They utilise
presupposed Lipschitz properties in order to compute inferences over unobserved
function values. Unfortunately, most of these approaches rely on exact
knowledge about the input space metric as well as about the Lipschitz constant.
Furthermore, existing techniques to estimate the Lipschitz constants from the
data are not robust to noise or seem to be ad-hoc and typically are decoupled
from the ultimate learning and prediction task. To overcome these limitations,
we propose an approach for optimising parameters of the presupposed metrics by
minimising validation set prediction errors. To avoid poor performance due to
local minima, we propose to utilise Lipschitz properties of the optimisation
objective to ensure global optimisation success. The resulting approach is a
new flexible method for nonparametric black-box learning. We provide
experimental evidence of the competitiveness of our approach on artificial as
well as on real data
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
Maximum Entropy Kernels for System Identification
A new nonparametric approach for system identification has been recently
proposed where the impulse response is modeled as the realization of a
zero-mean Gaussian process whose covariance (kernel) has to be estimated from
data. In this scheme, quality of the estimates crucially depends on the
parametrization of the covariance of the Gaussian process. A family of kernels
that have been shown to be particularly effective in the system identification
framework is the family of Diagonal/Correlated (DC) kernels. Maximum entropy
properties of a related family of kernels, the Tuned/Correlated (TC) kernels,
have been recently pointed out in the literature. In this paper we show that
maximum entropy properties indeed extend to the whole family of DC kernels. The
maximum entropy interpretation can be exploited in conjunction with results on
matrix completion problems in the graphical models literature to shed light on
the structure of the DC kernel. In particular, we prove that the DC kernel
admits a closed-form factorization, inverse and determinant. These results can
be exploited both to improve the numerical stability and to reduce the
computational complexity associated with the computation of the DC estimator.Comment: Extends results of 2014 IEEE MSC Conference Proceedings
(arXiv:1406.5706
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