4,740 research outputs found
In All Likelihood, Deep Belief Is Not Enough
Statistical models of natural stimuli provide an important tool for
researchers in the fields of machine learning and computational neuroscience. A
canonical way to quantitatively assess and compare the performance of
statistical models is given by the likelihood. One class of statistical models
which has recently gained increasing popularity and has been applied to a
variety of complex data are deep belief networks. Analyses of these models,
however, have been typically limited to qualitative analyses based on samples
due to the computationally intractable nature of the model likelihood.
Motivated by these circumstances, the present article provides a consistent
estimator for the likelihood that is both computationally tractable and simple
to apply in practice. Using this estimator, a deep belief network which has
been suggested for the modeling of natural image patches is quantitatively
investigated and compared to other models of natural image patches. Contrary to
earlier claims based on qualitative results, the results presented in this
article provide evidence that the model under investigation is not a
particularly good model for natural image
A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines
Restricted Boltzmann machines (RBMs) are energy-based neural-networks which
are commonly used as the building blocks for deep architectures neural
architectures. In this work, we derive a deterministic framework for the
training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer
(TAP) mean-field approximation of widely-connected systems with weak
interactions coming from spin-glass theory. While the TAP approach has been
extensively studied for fully-visible binary spin systems, our construction is
generalized to latent-variable models, as well as to arbitrarily distributed
real-valued spin systems with bounded support. In our numerical experiments, we
demonstrate the effective deterministic training of our proposed models and are
able to show interesting features of unsupervised learning which could not be
directly observed with sampling. Additionally, we demonstrate how to utilize
our TAP-based framework for leveraging trained RBMs as joint priors in
denoising problems
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
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