Zobrazeno 1 - 10
of 5 042
pro vyhledávání: '"TEC"'
Autor:
Bernárdez, Guillermo, Telyatnikov, Lev, Montagna, Marco, Baccini, Federica, Papillon, Mathilde, Ferriol-Galmés, Miquel, Hajij, Mustafa, Papamarkou, Theodore, Bucarelli, Maria Sofia, Zaghen, Olga, Mathe, Johan, Myers, Audun, Mahan, Scott, Lillemark, Hansen, Vadgama, Sharvaree, Bekkers, Erik, Doster, Tim, Emerson, Tegan, Kvinge, Henry, Agate, Katrina, Ahmed, Nesreen K, Bai, Pengfei, Banf, Michael, Battiloro, Claudio, Beketov, Maxim, Bogdan, Paul, Carrasco, Martin, Cavallo, Andrea, Choi, Yun Young, Dasoulas, George, Elphick, Matouš, Escalona, Giordan, Filipiak, Dominik, Fritze, Halley, Gebhart, Thomas, Gil-Sorribes, Manel, Goomanee, Salvish, Guallar, Victor, Imasheva, Liliya, Irimia, Andrei, Jin, Hongwei, Johnson, Graham, Kanakaris, Nikos, Koloski, Boshko, Kovač, Veljko, Lecha, Manuel, Lee, Minho, Leroy, Pierrick, Long, Theodore, Magai, German, Martinez, Alvaro, Masden, Marissa, Mežnar, Sebastian, Miquel-Oliver, Bertran, Molina, Alexis, Nikitin, Alexander, Nurisso, Marco, Piekenbrock, Matt, Qin, Yu, Rygiel, Patryk, Salatiello, Alessandro, Schattauer, Max, Snopov, Pavel, Suk, Julian, Sánchez, Valentina, Tec, Mauricio, Vaccarino, Francesco, Verhellen, Jonas, Wantiez, Frederic, Weers, Alexander, Zajec, Patrik, Škrlj, Blaž, Miolane, Nina
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem
Externí odkaz:
http://arxiv.org/abs/2409.05211
Autor:
Dr. C. Leónides Castellanos González, Ing. Néstor E. Céspedes Novo, Lic. Alexandra Sequeda Serrano, Tec. José Enrique Jaime Mendosa, Lic. Lady Johana Niño Vera
Publikováno v:
Agroecosistemas, Vol 6, Iss 3, Pp 57-65 (2018)
El objetivo de la presente investigación fue caracterizar microbiológicamente seis biopreparados que se producen en la de Granja Agrobiológica Sol Vida en Pamplona a los que se les atribuyen efectos biofertilizantes, bioestimulantes y antagonistas
Externí odkaz:
https://doaj.org/article/e45f1cac3ed24db7a5cb0768c7cd0ae5
Autor:
Battiloro, Claudio, Karaismailoğlu, Ege, Tec, Mauricio, Dasoulas, George, Audirac, Michelle, Dominici, Francesca
Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emerged recently as a promising tool for addressing this issue. TDL enable
Externí odkaz:
http://arxiv.org/abs/2405.15429
Autor:
Considine, Ellen M., Nethery, Rachel C., Wellenius, Gregory A., Dominici, Francesca, Tec, Mauricio
A key strategy in societal adaptation to climate change is the use of alert systems to reduce the adverse health impacts of extreme heat events by prompting preventative action. In this work, we investigate reinforcement learning (RL) as a tool to op
Externí odkaz:
http://arxiv.org/abs/2312.14196
Autor:
Tec, Mauricio, Trisovic, Ana, Audirac, Michelle, Woodward, Sophie, Hu, Jie Kate, Khoshnevis, Naeem, Dominici, Francesca
Spatial confounding poses a significant challenge in scientific studies involving spatial data, where unobserved spatial variables can influence both treatment and outcome, possibly leading to spurious associations. To address this problem, we introd
Externí odkaz:
http://arxiv.org/abs/2312.00710
Publikováno v:
ACM KDD 2024
A fundamental task in causal inference is estimating the effect of distribution shift in the treatment variable. We refer to this problem as shift-response function (SRF) estimation. Existing neural network methods for causal inference lack theoretic
Externí odkaz:
http://arxiv.org/abs/2302.02560
A fundamental task in science is to design experiments that yield valuable insights about the system under study. Mathematically, these insights can be represented as a utility or risk function that shapes the value of conducting each experiment. We
Externí odkaz:
http://arxiv.org/abs/2210.12122
Publikováno v:
AAAI 2023
Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are dictated in part
Externí odkaz:
http://arxiv.org/abs/2209.12316