151,656 research outputs found
Probabilistic Label Relation Graphs with Ising Models
We consider classification problems in which the label space has structure. A
common example is hierarchical label spaces, corresponding to the case where
one label subsumes another (e.g., animal subsumes dog). But labels can also be
mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To
jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy
and exclusion) graph was introduced in [7]. This combined a conditional random
field (CRF) with a deep neural network (DNN), resulting in state of the art
results when applied to visual object classification problems where the
training labels were drawn from different levels of the ImageNet hierarchy
(e.g., an image might be labeled with the basic level category "dog", rather
than the more specific label "husky"). In this paper, we extend the HEX model
to allow for soft or probabilistic relations between labels, which is useful
when there is uncertainty about the relationship between two labels (e.g., an
antelope is "sort of" furry, but not to the same degree as a grizzly bear). We
call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can
be converted to an Ising model, which allows us to use existing off-the-shelf
inference methods (in contrast to the HEX method, which needed specialized
inference algorithms). Experimental results show significant improvements in a
number of large-scale visual object classification tasks, outperforming the
previous HEX model.Comment: International Conference on Computer Vision (2015
Bethe Projections for Non-Local Inference
Many inference problems in structured prediction are naturally solved by
augmenting a tractable dependency structure with complex, non-local auxiliary
objectives. This includes the mean field family of variational inference
algorithms, soft- or hard-constrained inference using Lagrangian relaxation or
linear programming, collective graphical models, and forms of semi-supervised
learning such as posterior regularization. We present a method to
discriminatively learn broad families of inference objectives, capturing
powerful non-local statistics of the latent variables, while maintaining
tractable and provably fast inference using non-Euclidean projected gradient
descent with a distance-generating function given by the Bethe entropy. We
demonstrate the performance and flexibility of our method by (1) extracting
structured citations from research papers by learning soft global constraints,
(2) achieving state-of-the-art results on a widely-used handwriting recognition
task using a novel learned non-convex inference procedure, and (3) providing a
fast and highly scalable algorithm for the challenging problem of inference in
a collective graphical model applied to bird migration.Comment: minor bug fix to appendix. appeared in UAI 201
Benchmarking for Bayesian Reinforcement Learning
In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise
the collected rewards while interacting with their environment while using some
prior knowledge that is accessed beforehand. Many BRL algorithms have already
been proposed, but even though a few toy examples exist in the literature,
there are still no extensive or rigorous benchmarks to compare them. The paper
addresses this problem, and provides a new BRL comparison methodology along
with the corresponding open source library. In this methodology, a comparison
criterion that measures the performance of algorithms on large sets of Markov
Decision Processes (MDPs) drawn from some probability distributions is defined.
In order to enable the comparison of non-anytime algorithms, our methodology
also includes a detailed analysis of the computation time requirement of each
algorithm. Our library is released with all source code and documentation: it
includes three test problems, each of which has two different prior
distributions, and seven state-of-the-art RL algorithms. Finally, our library
is illustrated by comparing all the available algorithms and the results are
discussed.Comment: 37 page
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