discussion ying nian wu ucla department of statistics jsm 2011
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Discussion Ying Nian Wu UCLA Department of Statistics JSM 2011. Population value decomposition. Latent variable models. Hidden. Observed. Learning:. Examples. Inference:. Latent variable models. Mixture model. Factor analysis. Computational neural science. Hidden. Observed. - PowerPoint PPT PresentationTRANSCRIPT
Discussion
Ying Nian WuUCLA Department of Statistics
JSM 2011
Population value decomposition
Latent variable models
Hidden
Observed
Learning: Examples
Inference:
Latent variable models Mixture model
Factor analysis
Computational neural science
Z: Internal representation by neurons
Y: Sensory data from outside environment
Hidden
Observed
Connection weights
Hierarchical extension: modeling Z by another layer of hidden variables explaining Y instead of Z
Inference / explaining away
Source: Scientific American, 1999
Visual cortex: layered hierarchical architecture
V1: primary visual cortex simple cells complex cells
bottom-up/top-down
Independent Component Analysis Bell and Sejnowski, 1996
CBcBcI NN B ...11
Nicpci ,...,1tly independen )(~
)dim(IN
IIC AB 1
mNNmmm CBcBcI B ,11, ...
mmm IIC AB 1
Laplacian/Cauchy
Hyvarinen, 2000
Sparse coding Olshausen and Field, 1996
Laplacian/Cauchy/mixture Gaussians
Nicpci ,...,1tly independen )(~
NNBcBcI ...11
mNNmmm BcBcI ,11, ...)dim(IN
Inference: sparsification, non-linear lasso/basis pursuit/matching pursuit mode and uncertainty of p(C|I) explaining-away, lateral inhibition
Nicpci ,...,1tly independen )(~
Sparse coding / variable selection
Learning: mNNmmm BcBcI ,11, ...
)dim(IN
A dictionary of representational elements (regressors)
NNBcBcI ...11
Olshausen and Field, 1996
}exp{)(
1),(
,, j
jiiji vhW
WZVHp
Nihi ,...,1 ,
V
Restricted Boltzmann Machine Hinton, Osindero and Teh, 2006
P(V|H)P(H|V): factorized no-explaining away
hidden, binary
visible
Source: Scientific American, 1999
Visual cortex: layered hierarchical architecture
bottom-up/top-down
What is beyond V1?Hierarchical model?
P(V,H) = P(H)P(V|H) P(H) P(V’,H)
I
H
V
V’
Discriminative correction by back-propagation
Unfolding, untying, re-learning
Hierarchical RBM Hinton, Osindero and Teh, 2006
Hierarchical sparse coding
NNBcBcI ...11
,,sxB
Attributed sparse coding elements transformation group topological neighborhood system
UBcIii sx
n
ii
,,
1
Layer above : further coding of the attributes of selected sparse coding elements
Active basis modelWu, Si, Gong, Zhu, 10Zhu, Guo, Wang, Xu, 05
n-stroke templaten = 40 to 60, box= 100x100
Learning and Inference
Finding n strokes to sketch M images simultaneouslyn = 60, M = 9
Scan over multiple resolutions
Scan over multiple resolutions and orientations (rotating template)
Learning active basis models from non-aligned imageEM-type maximum likelihood learning, Initialized by single image learning
Learning active basis models from non-aligned image
Learning active basis models from non-aligned image
Hierarchical active basis
Lowlog-likelihood
Highlog-like
Model based clustering
MNIST500 total