559 research outputs found
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets
In this paper, we study the effects of different prior and likelihood choices
for Bayesian matrix factorisation, focusing on small datasets. These choices
can greatly influence the predictive performance of the methods. We identify
four groups of approaches: Gaussian-likelihood with real-valued priors,
nonnegative priors, semi-nonnegative models, and finally Poisson-likelihood
approaches. For each group we review several models from the literature,
considering sixteen in total, and discuss the relations between different
priors and matrix norms. We extensively compare these methods on eight
real-world datasets across three application areas, giving both inter- and
intra-group comparisons. We measure convergence runtime speed, cross-validation
performance, sparse and noisy prediction performance, and model selection
robustness. We offer several insights into the trade-offs between prior and
likelihood choices for Bayesian matrix factorisation on small datasets - such
as that Poisson models give poor predictions, and that nonnegative models are
more constrained than real-valued ones
A Survey on Bayesian Deep Learning
A comprehensive artificial intelligence system needs to not only perceive the
environment with different `senses' (e.g., seeing and hearing) but also infer
the world's conditional (or even causal) relations and corresponding
uncertainty. The past decade has seen major advances in many perception tasks
such as visual object recognition and speech recognition using deep learning
models. For higher-level inference, however, probabilistic graphical models
with their Bayesian nature are still more powerful and flexible. In recent
years, Bayesian deep learning has emerged as a unified probabilistic framework
to tightly integrate deep learning and Bayesian models. In this general
framework, the perception of text or images using deep learning can boost the
performance of higher-level inference and in turn, the feedback from the
inference process is able to enhance the perception of text or images. This
survey provides a comprehensive introduction to Bayesian deep learning and
reviews its recent applications on recommender systems, topic models, control,
etc. Besides, we also discuss the relationship and differences between Bayesian
deep learning and other related topics such as Bayesian treatment of neural
networks.Comment: To appear in ACM Computing Surveys (CSUR) 202
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