Inference from Evolving Populations: Agriculture
26 mins 2 secs,
47.63 MB,
MP3
44100 Hz,
249.78 kbits/sec
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About this item
| Description: |
Lemercier, M
Tuesday, March 16, 2021 - 11:00 to 11:25 |
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| Created: | 2021-03-17 12:43 |
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| Collection: | Unlocking Data Streams |
| Publisher: | Isaac Newton Institute |
| Copyright: | Lemercier, M |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Inferring properties about time-evolving populations is a widespread problem, yet a non-standard machine learning task. Most existing machine learning models can either handle a static snapshot of a population or a single trajectory. In this talk I will present a generic framework, based on the expected signature which enables to compactly summarize a cloud of time series and make decisions on it. I will discuss an application in agricultural monitoring, where a key challenge is to predict the yield before harvest using a collection of time series acquired by satellite-sensors. |
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Available Formats
| Format | Quality | Bitrate | Size | |||
|---|---|---|---|---|---|---|
| MPEG-4 Video | 640x360 | 316.23 kbits/sec | 60.22 MB | View | Download | |
| WebM | 640x360 | 215.35 kbits/sec | 41.04 MB | View | Download | |
| iPod Video | 480x270 | 460.52 kbits/sec | 87.70 MB | View | Download | |
| MP3 * | 44100 Hz | 249.78 kbits/sec | 47.63 MB | Listen | Download | |
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