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» Unsupervised Learning of Image Transformations
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JMLR
2012
11 years 7 months ago
Deep Learning Made Easier by Linear Transformations in Perceptrons
We transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero output and zero slope on average, and use separate shortcut connections to model th...
Tapani Raiko, Harri Valpola, Yann LeCun
CVPR
2005
IEEE
14 years 7 months ago
Joint Nonparametric Alignment for Analyzing Spatial Gene Expression Patterns in Drosophila Imaginal Discs
To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing i...
Parvez Ahammad, Cyrus L. Harmon, Ann Hammonds, Sha...
ISBI
2009
IEEE
14 years 3 days ago
Quantitative Comparison of Spot Detection Methods in Live-Cell Fluorescence Microscopy Imaging
In live-cell fluorescence microscopy imaging, quantitative analysis of biological image data generally involves the detection of many subresolution objects, appearing as diffract...
Ihor Smal, Marco Loog, Wiro J. Niessen, Erik H. W....
JBI
2007
148views Bioinformatics» more  JBI 2007»
13 years 5 months ago
A method for linking computed image features to histological semantics in neuropathology
In medical image analysis, the image content is often represented by computed features that need to be interpreted at a clinical level of understanding to support lopment of clini...
Birgit Lessmann, Tim W. Nattkemper, V. H. Hans, An...
ICCV
2011
IEEE
12 years 5 months ago
Gradient-based learning of higher-order image features
Recent work on unsupervised feature learning has shown that learning on polynomial expansions of input patches, such as on pair-wise products of pixel intensities, can improve the...
Roland Memisevic