41,645 research outputs found
Learning Bayesian Networks with the bnlearn R Package
bnlearn is an R package which includes several algorithms for learning the
structure of Bayesian networks with either discrete or continuous variables.
Both constraint-based and score-based algorithms are implemented, and can use
the functionality provided by the snow package to improve their performance via
parallel computing. Several network scores and conditional independence
algorithms are available for both the learning algorithms and independent use.
Advanced plotting options are provided by the Rgraphviz package.Comment: 22 pages, 4 picture
Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.
Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox-called seqNMF-with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral datas. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs
The Missing Data Encoder: Cross-Channel Image Completion\\with Hide-And-Seek Adversarial Network
Image completion is the problem of generating whole images from fragments
only. It encompasses inpainting (generating a patch given its surrounding),
reverse inpainting/extrapolation (generating the periphery given the central
patch) as well as colorization (generating one or several channels given other
ones). In this paper, we employ a deep network to perform image completion,
with adversarial training as well as perceptual and completion losses, and call
it the ``missing data encoder'' (MDE). We consider several configurations based
on how the seed fragments are chosen. We show that training MDE for ``random
extrapolation and colorization'' (MDE-REC), i.e. using random
channel-independent fragments, allows a better capture of the image semantics
and geometry. MDE training makes use of a novel ``hide-and-seek'' adversarial
loss, where the discriminator seeks the original non-masked regions, while the
generator tries to hide them. We validate our models both qualitatively and
quantitatively on several datasets, showing their interest for image
completion, unsupervised representation learning as well as face occlusion
handling
The IBMAP approach for Markov networks structure learning
In this work we consider the problem of learning the structure of Markov
networks from data. We present an approach for tackling this problem called
IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC
algorithm, designed for avoiding important limitations of existing
independence-based algorithms. These algorithms proceed by performing
statistical independence tests on data, trusting completely the outcome of each
test. In practice tests may be incorrect, resulting in potential cascading
errors and the consequent reduction in the quality of the structures learned.
IBMAP contemplates this uncertainty in the outcome of the tests through a
probabilistic maximum-a-posteriori approach. The approach is instantiated in
the IBMAP-HC algorithm, a structure selection strategy that performs a
polynomial heuristic local search in the space of possible structures. We
present an extensive empirical evaluation on synthetic and real data, showing
that our algorithm outperforms significantly the current independence-based
algorithms, in terms of data efficiency and quality of learned structures, with
equivalent computational complexities. We also show the performance of IBMAP-HC
in a real-world application of knowledge discovery: EDAs, which are
evolutionary algorithms that use structure learning on each generation for
modeling the distribution of populations. The experiments show that when
IBMAP-HC is used to learn the structure, EDAs improve the convergence to the
optimum
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