29,726 research outputs found
Reconstructing dynamical networks via feature ranking
Empirical data on real complex systems are becoming increasingly available.
Parallel to this is the need for new methods of reconstructing (inferring) the
topology of networks from time-resolved observations of their node-dynamics.
The methods based on physical insights often rely on strong assumptions about
the properties and dynamics of the scrutinized network. Here, we use the
insights from machine learning to design a new method of network reconstruction
that essentially makes no such assumptions. Specifically, we interpret the
available trajectories (data) as features, and use two independent feature
ranking approaches -- Random forest and RReliefF -- to rank the importance of
each node for predicting the value of each other node, which yields the
reconstructed adjacency matrix. We show that our method is fairly robust to
coupling strength, system size, trajectory length and noise. We also find that
the reconstruction quality strongly depends on the dynamical regime
Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection
Automated segmentation approaches are crucial to quantitatively analyze
large-scale 3D microscopy images. Particularly in deep tissue regions,
automatic methods still fail to provide error-free segmentations. To improve
the segmentation quality throughout imaged samples, we present a new
supervoxel-based 3D segmentation approach that outperforms current methods and
reduces the manual correction effort. The algorithm consists of gentle
preprocessing and a conservative super-voxel generation method followed by
supervoxel agglomeration based on local signal properties and a postprocessing
step to fix under-segmentation errors using a Convolutional Neural Network. We
validate the functionality of the algorithm on manually labeled 3D confocal
images of the plant Arabidopis thaliana and compare the results to a
state-of-the-art meristem segmentation algorithm.Comment: 5 pages, 3 figures, 1 tabl
Learning Word Representations with Hierarchical Sparse Coding
We propose a new method for learning word representations using hierarchical
regularization in sparse coding inspired by the linguistic study of word
meanings. We show an efficient learning algorithm based on stochastic proximal
methods that is significantly faster than previous approaches, making it
possible to perform hierarchical sparse coding on a corpus of billions of word
tokens. Experiments on various benchmark tasks---word similarity ranking,
analogies, sentence completion, and sentiment analysis---demonstrate that the
method outperforms or is competitive with state-of-the-art methods. Our word
representations are available at
\url{http://www.ark.cs.cmu.edu/dyogatam/wordvecs/}
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