380 research outputs found
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architecture. 3) to suggest future research directions and applications.Comment: Unpublished manuscript, 17 page
"Mental Rotation" by Optimizing Transforming Distance
The human visual system is able to recognize objects despite transformations
that can drastically alter their appearance. To this end, much effort has been
devoted to the invariance properties of recognition systems. Invariance can be
engineered (e.g. convolutional nets), or learned from data explicitly (e.g.
temporal coherence) or implicitly (e.g. by data augmentation). One idea that
has not, to date, been explored is the integration of latent variables which
permit a search over a learned space of transformations. Motivated by evidence
that people mentally simulate transformations in space while comparing
examples, so-called "mental rotation", we propose a transforming distance.
Here, a trained relational model actively transforms pairs of examples so that
they are maximally similar in some feature space yet respect the learned
transformational constraints. We apply our method to nearest-neighbour problems
on the Toronto Face Database and NORB
Scoring and Classifying with Gated Auto-encoders
Auto-encoders are perhaps the best-known non-probabilistic methods for
representation learning. They are conceptually simple and easy to train. Recent
theoretical work has shed light on their ability to capture manifold structure,
and drawn connections to density modelling. This has motivated researchers to
seek ways of auto-encoder scoring, which has furthered their use in
classification. Gated auto-encoders (GAEs) are an interesting and flexible
extension of auto-encoders which can learn transformations among different
images or pixel covariances within images. However, they have been much less
studied, theoretically or empirically. In this work, we apply a dynamical
systems view to GAEs, deriving a scoring function, and drawing connections to
Restricted Boltzmann Machines. On a set of deep learning benchmarks, we also
demonstrate their effectiveness for single and multi-label classification
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