1,292 research outputs found
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
Gated networks: an inventory
Gated networks are networks that contain gating connections, in which the
outputs of at least two neurons are multiplied. Initially, gated networks were
used to learn relationships between two input sources, such as pixels from two
images. More recently, they have been applied to learning activity recognition
or multi-modal representations. The aims of this paper are threefold: 1) to
explain the basic computations in gated networks to the non-expert, while
adopting a standpoint that insists on their symmetric nature. 2) to serve as a
quick reference guide to the recent literature, by providing an inventory of
applications of these networks, as well as recent extensions to the basic
architecture. 3) to suggest future research directions and applications.Comment: Unpublished manuscript, 17 page
Neural Ideal Point Estimation Network
Understanding politics is challenging because the politics take the influence
from everything. Even we limit ourselves to the political context in the
legislative processes; we need a better understanding of latent factors, such
as legislators, bills, their ideal points, and their relations. From the
modeling perspective, this is difficult 1) because these observations lie in a
high dimension that requires learning on low dimensional representations, and
2) because these observations require complex probabilistic modeling with
latent variables to reflect the causalities. This paper presents a new model to
reflect and understand this political setting, NIPEN, including factors
mentioned above in the legislation. We propose two versions of NIPEN: one is a
hybrid model of deep learning and probabilistic graphical model, and the other
model is a neural tensor model. Our result indicates that NIPEN successfully
learns the manifold of the legislative bill texts, and NIPEN utilizes the
learned low-dimensional latent variables to increase the prediction performance
of legislators' votings. Additionally, by virtue of being a domain-rich
probabilistic model, NIPEN shows the hidden strength of the legislators' trust
network and their various characteristics on casting votes
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