meeting this Friday

[Leif Johnson]

Hi everyone -

Our next meeting will be this Friday at 10am in GDC 3.516. We'll have
some bagels and discuss the following paper:

http://www.cs.toronto.edu/~rsalakhu/papers/Multimodal_DBM.pdf

Multimodal Learning with Deep Boltzmann Machines

A Deep Boltzmann Machine is described for learning a generative model of data
that consists of multiple and diverse input modalities. The model can be used
to extract a unified representation that fuses modalities together. We find that
this representation is useful for classification and information
retrieval tasks. The
model works by learning a probability density over the space of
multimodal inputs.
It uses states of latent variables as representations of the input.
The model can
extract this representation even when some modalities are absent by sampling
from the conditional distribution over them and filling them in. Our
experimental
results on bi-modal data consisting of images and text show that the Multimodal
DBM can learn a good generative model of the joint space of image and text
inputs that is useful for information retrieval from both unimodal and
multimodal
queries. We further demonstrate that this model significantly outperforms SVMs
and LDA on discriminative tasks. Finally, we compare our model to other deep
learning methods, including autoencoders and deep belief networks, and show that
it achieves noticeable gains.

lmj

--
http://www.cs.utexas.edu/~leif