Difference between revisions of "Fall 2015 Schedule"
From CompSemWiki
Jump to navigationJump to searchCompSemUser (talk | contribs) |
CompSemUser (talk | contribs) |
||
| Line 4: | Line 4: | ||
|2015.9.2 || No meeting | |2015.9.2 || No meeting | ||
|- | |- | ||
| − | |2015.9.9 || Eero Hyvönen | + | |2015.9.9 || Eero Hyvönen - http://www.seco.tkk.fi/u/eahyvone/ - "Cultural Heritage Linked Data on the Semantic Web." Cultural Heritage (CH) (meta)data is often heterogeneous, multilingual, distributed, semantically interlinked, and produced independently by organizations and individuals using different schemas, tools, and practices. As a result, a fundamental problem area in dealing with CH data is to make the content mutually interoperable, so that it can be searched, linked, and presented in a harmonized way across the boundaries of the datasets and data silos. Semantic Web and Linked Data standards and practices of W3C are a promising approach to address these issues [1]. However, this is not enough: we also need a content infrastructure, i.e., the actual domain ontologies, metadata models, and data shared by the CH community, and web services that make their integration and use in CH data systems easy and cost efficient. |
| + | This talk tells about our experiences in building a national level Linked Data content infrastructure in Finland. | ||
|- | |- | ||
|2015.9.16 || Stephen Becker - "Matrix Completion and Robust PCA: new data analysis tools". Matrix completion is a generalization of compressed sensing that seeks to determine missing matrix entries under some (non-Bayesian) assumptions about the matrix. The technique has generated a lot of excitement due to rigorous guarantees in some case, and also due to applications to machine learning (e.g., the Netflix prize problem). This talk discusses basic matrix completion, including efficient algorithms suitable for big data, as well as an extension of matrix completion known as robust PCA, which can handle large outliers in the data. We continue with several applications: inferring the structure of chromosomes, functional imaging of the brain, removing clouds from multi-spectral satellite image data, and verifying the properties of a quantum state or a quantum gate. http://amath.colorado.edu/faculty/becker/ | |2015.9.16 || Stephen Becker - "Matrix Completion and Robust PCA: new data analysis tools". Matrix completion is a generalization of compressed sensing that seeks to determine missing matrix entries under some (non-Bayesian) assumptions about the matrix. The technique has generated a lot of excitement due to rigorous guarantees in some case, and also due to applications to machine learning (e.g., the Netflix prize problem). This talk discusses basic matrix completion, including efficient algorithms suitable for big data, as well as an extension of matrix completion known as robust PCA, which can handle large outliers in the data. We continue with several applications: inferring the structure of chromosomes, functional imaging of the brain, removing clouds from multi-spectral satellite image data, and verifying the properties of a quantum state or a quantum gate. http://amath.colorado.edu/faculty/becker/ | ||
Revision as of 13:07, 30 July 2015
| 2015.8.26 | Min Zhang - Title / Abstract to Follow |
| 2015.9.2 | No meeting |
| 2015.9.9 | Eero Hyvönen - http://www.seco.tkk.fi/u/eahyvone/ - "Cultural Heritage Linked Data on the Semantic Web." Cultural Heritage (CH) (meta)data is often heterogeneous, multilingual, distributed, semantically interlinked, and produced independently by organizations and individuals using different schemas, tools, and practices. As a result, a fundamental problem area in dealing with CH data is to make the content mutually interoperable, so that it can be searched, linked, and presented in a harmonized way across the boundaries of the datasets and data silos. Semantic Web and Linked Data standards and practices of W3C are a promising approach to address these issues [1]. However, this is not enough: we also need a content infrastructure, i.e., the actual domain ontologies, metadata models, and data shared by the CH community, and web services that make their integration and use in CH data systems easy and cost efficient.
This talk tells about our experiences in building a national level Linked Data content infrastructure in Finland. |
| 2015.9.16 | Stephen Becker - "Matrix Completion and Robust PCA: new data analysis tools". Matrix completion is a generalization of compressed sensing that seeks to determine missing matrix entries under some (non-Bayesian) assumptions about the matrix. The technique has generated a lot of excitement due to rigorous guarantees in some case, and also due to applications to machine learning (e.g., the Netflix prize problem). This talk discusses basic matrix completion, including efficient algorithms suitable for big data, as well as an extension of matrix completion known as robust PCA, which can handle large outliers in the data. We continue with several applications: inferring the structure of chromosomes, functional imaging of the brain, removing clouds from multi-spectral satellite image data, and verifying the properties of a quantum state or a quantum gate. http://amath.colorado.edu/faculty/becker/ |
| 2015.9.23 | |
| 2015.9.30 | |
| 2015.10.7 | N-minute madness |
| 2015.10.14 | |
| 2015.10.21 | |
| 2015.10.21 | |
| 2015.10.28 | |
| 2015.11.4 | |
| 2015.11.11 | |
| 2015.11.18 | |
| 2015.11.25 | Fall break |
| 2015.12.2 | |
| 2015.12.9 |
