995 research outputs found
Message Passing Algorithms for Compressed Sensing
Compressed sensing aims to undersample certain high-dimensional signals, yet
accurately reconstruct them by exploiting signal characteristics. Accurate
reconstruction is possible when the object to be recovered is sufficiently
sparse in a known basis. Currently, the best known sparsity-undersampling
tradeoff is achieved when reconstructing by convex optimization -- which is
expensive in important large-scale applications. Fast iterative thresholding
algorithms have been intensively studied as alternatives to convex optimization
for large-scale problems. Unfortunately known fast algorithms offer
substantially worse sparsity-undersampling tradeoffs than convex optimization.
We introduce a simple costless modification to iterative thresholding making
the sparsity-undersampling tradeoff of the new algorithms equivalent to that of
the corresponding convex optimization procedures. The new
iterative-thresholding algorithms are inspired by belief propagation in
graphical models. Our empirical measurements of the sparsity-undersampling
tradeoff for the new algorithms agree with theoretical calculations. We show
that a state evolution formalism correctly derives the true
sparsity-undersampling tradeoff. There is a surprising agreement between
earlier calculations based on random convex polytopes and this new, apparently
very different theoretical formalism.Comment: 6 pages paper + 9 pages supplementary information, 13 eps figure.
Submitted to Proc. Natl. Acad. Sci. US
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems
Crowdsourcing systems, in which numerous tasks are electronically distributed
to numerous "information piece-workers", have emerged as an effective paradigm
for human-powered solving of large scale problems in domains such as image
classification, data entry, optical character recognition, recommendation, and
proofreading. Because these low-paid workers can be unreliable, nearly all such
systems must devise schemes to increase confidence in their answers, typically
by assigning each task multiple times and combining the answers in an
appropriate manner, e.g. majority voting.
In this paper, we consider a general model of such crowdsourcing tasks and
pose the problem of minimizing the total price (i.e., number of task
assignments) that must be paid to achieve a target overall reliability. We give
a new algorithm for deciding which tasks to assign to which workers and for
inferring correct answers from the workers' answers. We show that our
algorithm, inspired by belief propagation and low-rank matrix approximation,
significantly outperforms majority voting and, in fact, is optimal through
comparison to an oracle that knows the reliability of every worker. Further, we
compare our approach with a more general class of algorithms which can
dynamically assign tasks. By adaptively deciding which questions to ask to the
next arriving worker, one might hope to reduce uncertainty more efficiently. We
show that, perhaps surprisingly, the minimum price necessary to achieve a
target reliability scales in the same manner under both adaptive and
non-adaptive scenarios. Hence, our non-adaptive approach is order-optimal under
both scenarios. This strongly relies on the fact that workers are fleeting and
can not be exploited. Therefore, architecturally, our results suggest that
building a reliable worker-reputation system is essential to fully harnessing
the potential of adaptive designs.Comment: 38 pages, 4 figur
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