1,065 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Surrogate-assisted parallel tempering for Bayesian neural learning
Due to the need for robust uncertainty quantification, Bayesian neural
learning has gained attention in the era of deep learning and big data. Markov
Chain Monte-Carlo (MCMC) methods typically implement Bayesian inference which
faces several challenges given a large number of parameters, complex and
multimodal posterior distributions, and computational complexity of large
neural network models. Parallel tempering MCMC addresses some of these
limitations given that they can sample multimodal posterior distributions and
utilize high-performance computing. However, certain challenges remain given
large neural network models and big data. Surrogate-assisted optimization
features the estimation of an objective function for models which are
computationally expensive. In this paper, we address the inefficiency of
parallel tempering MCMC for large-scale problems by combining parallel
computing features with surrogate assisted likelihood estimation that describes
the plausibility of a model parameter value, given specific observed data.
Hence, we present surrogate-assisted parallel tempering for Bayesian neural
learning for simple to computationally expensive models. Our results
demonstrate that the methodology significantly lowers the computational cost
while maintaining quality in decision making with Bayesian neural networks. The
method has applications for a Bayesian inversion and uncertainty quantification
for a broad range of numerical models.Comment: Engineering Applications of Artificial Intelligenc
Evolutionary multi-objective optimization using neural-based estimation of distribution algorithms
Ph.DDOCTOR OF PHILOSOPH
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