Výsledky vyhledávání

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Sims: An Interactive Tool for Geospatial Matching and Clustering

Autor: Zaytar, Akram, Tadesse, Girmaw Abebe, Robinson, Caleb, Bendito, Eduardo G., Devare, Medha, Chernet, Meklit, Hacheme, Gilles Q., Dodhia, Rahul, Ferres, Juan M. Lavista

Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains. This challenge hinders the rapid discovery of predictive features, which is essential for advancing geospat

Externí odkaz: http://arxiv.org/abs/2412.10184

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Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps

Autor: Tadesse, Girmaw Abebe, Robinson, Caleb, Mwangi, Charles, Maina, Esther, Nyakundi, Joshua, Marotti, Luana, Hacheme, Gilles Quentin, Alemohammad, Hamed, Dodhia, Rahul, Ferres, Juan M. Lavista

In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps provide crucial insights for addressing food insecurity by improving agricultural efforts,

Externí odkaz: http://arxiv.org/abs/2412.00777

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AI and the Future of Work in Africa White Paper

Autor: O'Neill, Jacki, Marivate, Vukosi, Glover, Barbara, Karanu, Winnie, Tadesse, Girmaw Abebe, Gyekye, Akua, Makena, Anne, Rosslyn-Smith, Wesley, Grollnek, Matthew, Wayua, Charity, Baguma, Rehema, Maduke, Angel, Spencer, Sarah, Kandie, Daniel, Maari, Dennis Ndege, Mutangana, Natasha, Axmed, Maxamed, Kamau, Nyambura, Adamu, Muhammad, Swaniker, Frank, Gatuguti, Brian, Donner, Jonathan, Graham, Mark, Mumo, Janet, Mbindyo, Caroline, N'Guessan, Charlette, Githinji, Irene, Makhafola, Lesego, Kruger, Sean, Etyang, Olivia, Onando, Mulang, Sevilla, Joe, Sambuli, Nanjira, Mbaya, Martin, Breloff, Paul, Anapey, Gideon M., Mogaleemang, Tebogo L., Nghonyama, Tiyani, Wanyoike, Muthoni, Mbuli, Bhekani, Nderu, Lawrence, Nyabero, Wambui, Alam, Uzma, Olaleye, Kayode, Njenga, Caroline, Sellen, Abigail, Kairo, David, Chabikwa, Rutendo, Abdulhamid, Najeeb G., Kubasu, Ketry, Okolo, Chinasa T., Akpo, Eugenia, Budu, Joel, Karambal, Issa, Berkoh, Joseph, Wasswa, William, Njagwi, Muchai, Burnet, Rob, Ochanda, Loise, de Bod, Hanlie, Ankrah, Elizabeth, Kinyunyu, Selemani, Kariuki, Mutembei, Kiyimba, Kizito, Eleshin, Farida, Madeje, Lillian Secelela, Muraga, Catherine, Nganga, Ida, Gichoya, Judy, Maina, Tabbz, Maina, Samuel, Mercy, Muchai, Ochieng, Millicent, Nyairo, Stephanie

This white paper is the output of a multidisciplinary workshop in Nairobi (Nov 2023). Led by a cross-organisational team including Microsoft Research, NEPAD, Lelapa AI, and University of Oxford. The workshop brought together diverse thought-leaders f

Externí odkaz: http://arxiv.org/abs/2411.10091

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ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding

Autor: Azime, Israel Abebe, Tonja, Atnafu Lambebo, Belay, Tadesse Destaw, Chanie, Yonas, Balcha, Bontu Fufa, Abadi, Negasi Haile, Ademtew, Henok Biadglign, Nerea, Mulubrhan Abebe, Yadeta, Debela Desalegn, Geremew, Derartu Dagne, tesfau, Assefa Atsbiha, Slusallek, Philipp, Solorio, Thamar, Klakow, Dietrich

With the rapid development of evaluation datasets to assess LLMs understanding across a wide range of subjects and domains, identifying a suitable language understanding benchmark has become increasingly challenging. In this work, we explore LLM eval

Externí odkaz: http://arxiv.org/abs/2411.05049

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Directly Optimizing Explanations for Desired Properties

Autor: Tadesse, Hiwot Belay, Hüyük, Alihan, Pan, Weiwei, Doshi-Velez, Finale

When explaining black-box machine learning models, it's often important for explanations to have certain desirable properties. Most existing methods `encourage' desirable properties in their construction of explanations. In this work, we demonstrate

Externí odkaz: http://arxiv.org/abs/2410.23880

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Economic Integration of Africa in the 21st Century: Complex Network and Panel Regression Analysis

Autor: Choramo, Tekilu Tadesse, Abafita, Jemal, Gandica, Yerali, Rocha, Luis E C

Global and regional integration has grown significantly in recent decades, boosting intra-African trade and positively impacting national economies through trade diversification and sustainable development. However, existing measures of economic inte

Externí odkaz: http://arxiv.org/abs/2410.21019

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Universal Spectral Transfer with Physical Prior-Informed Deep Generative Learning

Autor: Zhu, Yanmin, Tadesse, Loza F.

Spectroscopy is a powerful analytical technique for characterizing matter across physical and biological realms1-5. However, its fundamental principle necessitates specialized instrumentation per physical phenomena probed, limiting broad adoption and

Externí odkaz: http://arxiv.org/abs/2407.16094

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