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Akademický článek
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Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract Recent large language models (LLMs), such as ChatGPT, have demonstrated remarkable prediction performance for a growing array of tasks. However, their proliferation into high-stakes domains and compute-limited settings has created a burgeoni
Externí odkaz:
https://doaj.org/article/13ca7afc0ee54abaaa0a5d434f1c0e11
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 13, Iss 7, Pp n/a-n/a (2021)
Abstract We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model that recursively predicts six key atmospheric variables with six‐hour time resolution. This computationally efficient model uses convolutional n
Externí odkaz:
https://doaj.org/article/51f98f8aa6dd418e82a085d7ccde7bc3
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 11, Iss 8, Pp 2680-2693 (2019)
Abstract We develop elementary weather prediction models using deep convolutional neural networks (CNNs) trained on past weather data to forecast one or two fundamental meteorological fields on a Northern Hemisphere grid with no explicit knowledge ab
Externí odkaz:
https://doaj.org/article/6490fd46ee6d471ea14a48f09a7d2aa2
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 9, Pp n/a-n/a (2020)
Abstract We present a significantly improved data‐driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework inclu
Externí odkaz:
https://doaj.org/article/fa2a0ca04aa7476185a92c28151468cc
Publikováno v:
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.
Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers understand what input variables can be changed. However, a recourse plan's
Autor:
Rich Caruana, Yin Lou
Publikováno v:
Journal of Computing and Natural Science. :121-129
Various challenges in real life are multi-objective and conflicting (i.e., alter concurrent optimization). This implies that a single objective is optimized based on another’s cost. The Multi-Objective Optimization (MOO) issues are challenging but
Autor:
Zifei Xu, Tomas M. Bosschieter, Hui Lan, Benjamin Lengerich, Harsha Nori, Kristin Sitcov, Ian Painter, Vivienne Souter, Rich Caruana
Publikováno v:
American Journal of Obstetrics and Gynecology. 228:S404-S405
Autor:
Tomas M. Bosschieter, Hui Lan, Zifei Xu, Benjamin Lengerich, Harsha Nori, Kristin Sitcov, Ian Painter, Rich Caruana, Vivienne Souter
Publikováno v:
American Journal of Obstetrics and Gynecology. 228:S403-S404
Autor:
Rich Caruana, Harsha Nori
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.