Connectionists: CFP: ICML 2011 Workshop Learning Architectures, Representations, and Optimization for Speech and Visual Information Processing

[Honglak Lee]

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                                   CALL FOR PAPER

                                 ICML 2011 Workshop
     Learning Architectures, Representations, and Optimization
             for Speech and Visual Information Processing

                 http://icml2011speechvision.wordpress.com/

                     Bellevue, Washington, USA, July 2, 2011

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Overview:

In recent years, there has been a lot of interest in algorithms that learn
hierarchical representations from unlabeled data. Unsupervised learning and
deep learning methods, such as sparse coding, restricted Boltzmann machines,
deep belief networks, convolutional architectures, recursive compositional
models, and hierarchical generative models, have been successfully applied
to a variety of tasks in computer vision and speech processing with highly
promising results. In this workshop, we will bring together researchers who
are interested in learning representations and architectures and in
developing efficient and robust optimization algorithms for speech and
visual information processing, review the recent technical progress, and
discuss the challenges and future research directions. Detailed topics of
presentations are expected to include (but not limited to) the followings:

       • Development of learning models, e.g., deep belief nets, deep neural
nets, deep Boltzmann machines, high-order sparse coding, hierarchical
generative models, temporal and/or recursive models with deep structure,
generative models motivated by physical processes of human speech production
and of natural image formation, discriminative models motivated by human
speech and visual perception, etc.

       • Algorithms for probabilistic inference, optimization strategies
when the objective is non-convex, and large-scale implementations associated
with the above models.

       • Learning biologically inspired feature hierarchies in human visual
and auditory signal processing.

       • Novel representations via the use of side information in
unsupervised feature learning, e.g., spatial correlations in image,
sequential dynamics and temporal/spectral correlations in speech, physical
constraints in speech production, perceptual constraints in vision, and
other additional prior knowledge, etc.

       • Theoretical understanding on the role of unsupervised feature
learning in building complex predictive models. Under which conditions does
the feature hierarchy provide a better regularization or achieve a higher
statistical efficiency?

       • Success, failures, and lessons learned  in real-world applications
including understanding of natural scenes, recognition of objects and
events, speech recognition under controlled environments, large-vocabulary
speech recognition under realistic acoustic environments, auditory coding of
speech and music, etc.

If you are interested in presenting your work, please submit an extended
abstract (1-2 pages in conference proceedings format) via email to
icml2011ws.visionspeech at gmail.com. Accepted contributions will be presented
as posters.


Potential list of invited speakers:

Andrew Ng (Stanford University)
Fei-Fei Li (Stanford University)
John Platt (Microsoft Research)
Xuedong Huang (Microsoft)
OTHER Invited SPEAKERS WAITING FOR CONFIRMATION

Key Dates:

Paper Submission Deadline: April 29, 2011
Paper Acceptance Notification: May 20, 2011
Camera Ready Submission: June 10, 2011
Workshop Date: July 02, 2011

Organizers

Li Deng, Microsoft Research
Honglak Lee, University of Michigan
Kai Yu, NEC Laboratories America
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