Zobrazeno 1 - 10
of 8 508
pro vyhledávání: '"Machine learning pipeline"'
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroim
Externí odkaz:
http://arxiv.org/abs/2409.08584
Autor:
Chan, Man Leong, McIver, Jess, Mahabal, Ashish, Messick, Cody, Haggard, Daryl, Raza, Nayyer, Lecoeuche, Yannick, Sutton, Patrick J., Ewing, Becca, Di Renzo, Francesco, Cabero, Miriam, Ng, Raymond, Coughlin, Michael W., Ghosh, Shaon, Godwin, Patrick
Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid re
Externí odkaz:
http://arxiv.org/abs/2408.06491
Autor:
Jain, Aditya, Cunha, Fagner, Bunsen, Michael, Pasi, Léonard, Viklund, Anna, Larrivée, Maxim, Rolnick, David
Publikováno v:
NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning
Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosystem services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Cam
Externí odkaz:
http://arxiv.org/abs/2406.13031
Autor:
Maroni, Gabriele, Stojceski, Filip, Pallante, Lorenzo, Deriu, Marco A., Piga, Dario, Grasso, Gianvito
Cell-penetrating peptides (CPPs) are powerful vectors for the intracellular delivery of a diverse array of therapeutic molecules. Despite their potential, the rational design of CPPs remains a challenging task that often requires extensive experiment
Externí odkaz:
http://arxiv.org/abs/2406.01617
Autor:
Marx, Ethan, Benoit, William, Gunny, Alec, Omer, Rafia, Chatterjee, Deep, Venterea, Ricco C., Wills, Lauren, Saleem, Muhammed, Moreno, Eric, Raikman, Ryan, Govorkova, Ekaterina, Rankin, Dylan, Coughlin, Michael W., Harris, Philip, Katsavounidis, Erik
The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks h
Externí odkaz:
http://arxiv.org/abs/2403.18661
Autor:
Urbanowicz, Ryan J., Bandhey, Harsh, Keenan, Brendan T., Maislin, Greg, Hwang, Sy, Mowery, Danielle L., Lynch, Shannon M., Mazzotti, Diego R., Han, Fang, Li, Qing Yun, Penzel, Thomas, Tufik, Sergio, Bittencourt, Lia, Gislason, Thorarinn, de Chazal, Philip, Singh, Bhajan, McArdle, Nigel, Chen, Ning-Hung, Pack, Allan, Schwab, Richard J., Cistulli, Peter A., Magalang, Ulysses J.
While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML applic
Externí odkaz:
http://arxiv.org/abs/2312.05461
Autor:
Böther, Maximilian, Robroek, Ties, Gsteiger, Viktor, Holzinger, Robin, Ma, Xianzhe, Tözün, Pınar, Klimovic, Ana
In real-world machine learning (ML) pipelines, datasets are continuously growing. Models must incorporate this new training data to improve generalization and adapt to potential distribution shifts. The cost of model retraining is proportional to how
Externí odkaz:
http://arxiv.org/abs/2312.06254
The COVID-19 pandemic has highlighted the dire necessity to improve public health literacy for societal resilience. YouTube, the largest video-sharing social media platform, provides a vast repository of user-generated health information in a multi-m
Externí odkaz:
http://arxiv.org/abs/2312.09425
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