| EPSRC Reference: |
EP/G051569/1 |
| Title: |
Efficient Learning of Deeply Layered Models |
| Principal Investigator: |
Salakhutdinov, Mr R |
| Other Investigators: |
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| Department: |
Engineering |
| Organisation: |
University of Cambridge |
| Scheme: |
Postdoc Research Fellowship |
| Starts: |
01 October 2009 |
Ends: |
30 September 2012 |
Value (£): |
235,975
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Fundamentals of Computing |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires deep architectures that involve many layers of non-linear processing. Therefore developing effective learning algorithms that could learn multiple layers of representation are of fundamental importance.My proposed research concentrates on developing new learning and inference algorithms for probabilistic models with deep architectures, that contain many layers of latent variables and millions of parameters. These models hold great promise for building intelligent systems and should allow us to substantially improve prediction performance on large-scale visual object and speech recognition, as well as information retrieval and natural language processing tasks.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.cam.ac.uk |