Massive scale Gaussian processes with GPflow
46 mins 29 secs,
177.78 MB,
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480x270,
29.97 fps,
44100 Hz,
522.19 kbits/sec
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About this item
| Description: |
Hensman, J
Tuesday 6th March 2018 - 14:00 to 14:45 |
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| Created: | 2018-03-07 13:46 |
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| Collection: | Uncertainty quantification for complex systems: theory and methodologies |
| Publisher: | Isaac Newton Institute |
| Copyright: | Hensman, J |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
|
| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | In this talk I'll give an overview of how machine learning techniques have been used to scale Gaussian process models to huge datasets. I'll also introduce GPflow, a software library for Gaussian processes that leverages the computational framework TensorFlow, which is more commonly used for deep learning. |
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| MPEG-4 Video | 640x360 | 1.94 Mbits/sec | 676.48 MB | View | Download | |
| WebM | 640x360 | 457.02 kbits/sec | 155.65 MB | View | Download | |
| iPod Video * | 480x270 | 522.19 kbits/sec | 177.78 MB | View | Download | |
| MP3 | 44100 Hz | 249.75 kbits/sec | 85.12 MB | Listen | Download | |
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