13,462 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
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
Explainability in Deep Reinforcement Learning
A large set of the explainable Artificial Intelligence (XAI) literature is
emerging on feature relevance techniques to explain a deep neural network (DNN)
output or explaining models that ingest image source data. However, assessing
how XAI techniques can help understand models beyond classification tasks, e.g.
for reinforcement learning (RL), has not been extensively studied. We review
recent works in the direction to attain Explainable Reinforcement Learning
(XRL), a relatively new subfield of Explainable Artificial Intelligence,
intended to be used in general public applications, with diverse audiences,
requiring ethical, responsible and trustable algorithms. In critical situations
where it is essential to justify and explain the agent's behaviour, better
explainability and interpretability of RL models could help gain scientific
insight on the inner workings of what is still considered a black box. We
evaluate mainly studies directly linking explainability to RL, and split these
into two categories according to the way the explanations are generated:
transparent algorithms and post-hoc explainaility. We also review the most
prominent XAI works from the lenses of how they could potentially enlighten the
further deployment of the latest advances in RL, in the demanding present and
future of everyday problems.Comment: Article accepted at Knowledge-Based System
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