19,246 research outputs found
MAP inference via Block-Coordinate Frank-Wolfe Algorithm
We present a new proximal bundle method for Maximum-A-Posteriori (MAP)
inference in structured energy minimization problems. The method optimizes a
Lagrangean relaxation of the original energy minimization problem using a multi
plane block-coordinate Frank-Wolfe method that takes advantage of the specific
structure of the Lagrangean decomposition. We show empirically that our method
outperforms state-of-the-art Lagrangean decomposition based algorithms on some
challenging Markov Random Field, multi-label discrete tomography and graph
matching problems
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
Barrier Frank-Wolfe for Marginal Inference
We introduce a globally-convergent algorithm for optimizing the
tree-reweighted (TRW) variational objective over the marginal polytope. The
algorithm is based on the conditional gradient method (Frank-Wolfe) and moves
pseudomarginals within the marginal polytope through repeated maximum a
posteriori (MAP) calls. This modular structure enables us to leverage black-box
MAP solvers (both exact and approximate) for variational inference, and obtains
more accurate results than tree-reweighted algorithms that optimize over the
local consistency relaxation. Theoretically, we bound the sub-optimality for
the proposed algorithm despite the TRW objective having unbounded gradients at
the boundary of the marginal polytope. Empirically, we demonstrate the
increased quality of results found by tightening the relaxation over the
marginal polytope as well as the spanning tree polytope on synthetic and
real-world instances.Comment: 25 pages, 12 figures, To appear in Neural Information Processing
Systems (NIPS) 2015, Corrected reference and cleaned up bibliograph
Progressive Mauve: Multiple alignment of genomes with gene flux and rearrangement
Multiple genome alignment remains a challenging problem. Effects of
recombination including rearrangement, segmental duplication, gain, and loss
can create a mosaic pattern of homology even among closely related organisms.
We describe a method to align two or more genomes that have undergone
large-scale recombination, particularly genomes that have undergone substantial
amounts of gene gain and loss (gene flux). The method utilizes a novel
alignment objective score, referred to as a sum-of-pairs breakpoint score. We
also apply a probabilistic alignment filtering method to remove erroneous
alignments of unrelated sequences, which are commonly observed in other genome
alignment methods. We describe new metrics for quantifying genome alignment
accuracy which measure the quality of rearrangement breakpoint predictions and
indel predictions. The progressive genome alignment algorithm demonstrates
markedly improved accuracy over previous approaches in situations where genomes
have undergone realistic amounts of genome rearrangement, gene gain, loss, and
duplication. We apply the progressive genome alignment algorithm to a set of 23
completely sequenced genomes from the genera Escherichia, Shigella, and
Salmonella. The 23 enterobacteria have an estimated 2.46Mbp of genomic content
conserved among all taxa and total unique content of 15.2Mbp. We document
substantial population-level variability among these organisms driven by
homologous recombination, gene gain, and gene loss. Free, open-source software
implementing the described genome alignment approach is available from
http://gel.ahabs.wisc.edu/mauve .Comment: Revision dated June 19, 200
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