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Supporting Food Security in Africa Using ML with Catherine Nakalembe - #611

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Manage episode 352058553 series 2355587
TWIML and Sam Charrington에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 TWIML and Sam Charrington 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Today we conclude our coverage of the 2022 NeurIPS series joined by Catherine Nakalembe, an associate research professor at the University of Maryland, and Africa Program Director under NASA Harvest. In our conversation with Catherine, we take a deep dive into her talk from the ML in the Physical Sciences workshop, Supporting Food Security in Africa using Machine Learning and Earth Observations. We discuss the broad challenges associated with food insecurity, as well as Catherine’s role and the priorities of Harvest Africa, a program focused on advancing innovative satellite-driven methods to produce automated within-season crop type and crop-specific condition products that support agricultural assessments. We explore some of the technical challenges of her work, including the limited, but growing, access to remote sensing and earth observation datasets and how the availability of that data has changed in recent years, the lack of benchmarks for the tasks she’s working on, examples of how they’ve applied techniques like multi-task learning and task-informed meta-learning, and much more.

The complete show notes for this episode can be found at twimlai.com/go/611.

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700 에피소드

Artwork
icon공유
 
Manage episode 352058553 series 2355587
TWIML and Sam Charrington에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 TWIML and Sam Charrington 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Today we conclude our coverage of the 2022 NeurIPS series joined by Catherine Nakalembe, an associate research professor at the University of Maryland, and Africa Program Director under NASA Harvest. In our conversation with Catherine, we take a deep dive into her talk from the ML in the Physical Sciences workshop, Supporting Food Security in Africa using Machine Learning and Earth Observations. We discuss the broad challenges associated with food insecurity, as well as Catherine’s role and the priorities of Harvest Africa, a program focused on advancing innovative satellite-driven methods to produce automated within-season crop type and crop-specific condition products that support agricultural assessments. We explore some of the technical challenges of her work, including the limited, but growing, access to remote sensing and earth observation datasets and how the availability of that data has changed in recent years, the lack of benchmarks for the tasks she’s working on, examples of how they’ve applied techniques like multi-task learning and task-informed meta-learning, and much more.

The complete show notes for this episode can be found at twimlai.com/go/611.

  continue reading

700 에피소드

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