4,693 research outputs found
Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling
Conventional methods of estimating latent behaviour generally use attitudinal
questions which are subjective and these survey questions may not always be
available. We hypothesize that an alternative approach can be used for latent
variable estimation through an undirected graphical models. For instance,
non-parametric artificial neural networks. In this study, we explore the use of
generative non-parametric modelling methods to estimate latent variables from
prior choice distribution without the conventional use of measurement
indicators. A restricted Boltzmann machine is used to represent latent
behaviour factors by analyzing the relationship information between the
observed choices and explanatory variables. The algorithm is adapted for latent
behaviour analysis in discrete choice scenario and we use a graphical approach
to evaluate and understand the semantic meaning from estimated parameter vector
values. We illustrate our methodology on a financial instrument choice dataset
and perform statistical analysis on parameter sensitivity and stability. Our
findings show that through non-parametric statistical tests, we can extract
useful latent information on the behaviour of latent constructs through machine
learning methods and present strong and significant influence on the choice
process. Furthermore, our modelling framework shows robustness in input
variability through sampling and validation
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
Prediction of rare feature combinations in population synthesis: Application of deep generative modelling
In population synthesis applications, when considering populations with many
attributes, a fundamental problem is the estimation of rare combinations of
feature attributes. Unsurprisingly, it is notably more difficult to reliably
representthe sparser regions of such multivariate distributions and in
particular combinations of attributes which are absent from the original
sample. In the literature this is commonly known as sampling zeros for which no
systematic solution has been proposed so far. In this paper, two machine
learning algorithms, from the family of deep generative models,are proposed for
the problem of population synthesis and with particular attention to the
problem of sampling zeros. Specifically, we introduce the Wasserstein
Generative Adversarial Network (WGAN) and the Variational Autoencoder(VAE), and
adapt these algorithms for a large-scale population synthesis application. The
models are implemented on a Danish travel survey with a feature-space of more
than 60 variables. The models are validated in a cross-validation scheme and a
set of new metrics for the evaluation of the sampling-zero problem is proposed.
Results show how these models are able to recover sampling zeros while keeping
the estimation of truly impossible combinations, the structural zeros, at a
comparatively low level. Particularly, for a low dimensional experiment, the
VAE, the marginal sampler and the fully random sampler generate 5%, 21% and
26%, respectively, more structural zeros per sampling zero generated by the
WGAN, while for a high dimensional case, these figures escalate to 44%, 2217%
and 170440%, respectively. This research directly supports the development of
agent-based systems and in particular cases where detailed socio-economic or
geographical representations are required
- …