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Download slides: mlss08au_freitas_asm.pdf (14.4 MB)
Monday, January 17, 2011
Monte Carlo Simulation for Statistical Inference, Model Selection and Decision Making
[Speech] A trajectory density mixture network for acoustic articulatory inversion mapping
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A trajectory mixture density network for acoustic articulatory inversion mapping.pdf (113 KB) Mixture Density Network
PRML Chapter 5.
Thursday, January 13, 2011
Friday, January 7, 2011
[DBN] Learning multiple layers of Features from tiny images
In this paper, there are more detailed equation derivations for RBM, especially the Gaussian-Bernoulli RBM.
Wednesday, January 5, 2011
Matlab v7.3 mat file and python
From: http://mloss.org/community/blog/2009/nov/19/matlabtm-73-file-format-is-actually-hdf5-and-can-b/
It looks like that matlab version 7.3 and later are capable of writing out objects in the so called matlab 7.3 file format. While at first glance it looks like another proprietary format - it seems to be in fact the Hierarchical Data Format version 5 or in short hdf5.
So you can do all sorts of neat things:
Lets create some matrix in matlab first and save it:
>> x=[[1,2,3];[4,5,6];[7,8,9]] x = 1 2 3 4 5 6 7 8 9 >> save -v7.3 x.mat x
Lets investigate that file from the shell:
$ h5ls x.mat x Dataset {3, 3} $ h5dump x.mat HDF5 "x.mat" { GROUP "/" { DATASET "x" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 3, 3 ) / ( 3, 3 ) } DATA { (0,0): 1, 4, 7, (1,0): 2, 5, 8, (2,0): 3, 6, 9 } ATTRIBUTE "MATLAB_class" { DATATYPE H5T_STRING { STRSIZE 6; STRPAD H5T_STR_NULLTERM; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SCALAR DATA { (0): "double" } } } } }And load it from python:
>>> import h5py >>> import numpy >>> f = h5py.File('x.mat') >>> x=f["x"] >>> x <HDF5 dataset "x": shape (3, 3), type "<f8"> >>> numpy.array(x) array([[ 1., 4., 7.], [ 2., 5., 8.], [ 3., 6., 9.]])
So it seems actually to be a good idea to use matlab's 7.3 format for interoperability.
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