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pro vyhledávání: '"Price, Layne"'
Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, l
Externí odkaz:
http://arxiv.org/abs/2304.10643
Representation learning for proteins has primarily focused on the global understanding of protein sequences regardless of their length. However, shorter proteins (known as peptides) take on distinct structures and functions compared to their longer c
Externí odkaz:
http://arxiv.org/abs/2211.06428
Autor:
Wu, Yulun, Barton, Robert A., Wang, Zichen, Ioannidis, Vassilis N., De Donno, Carlo, Price, Layne C., Voloch, Luis F., Karypis, George
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variational Bayesian causal inference framework to predict a cell's gene expre
Externí odkaz:
http://arxiv.org/abs/2210.00116
Autor:
Wu, Yulun, Price, Layne C., Wang, Zichen, Ioannidis, Vassilis N., Barton, Robert A., Karypis, George
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, hu
Externí odkaz:
http://arxiv.org/abs/2209.05935
Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters. While various emulation schemes exist to approximate these slow calculations, these approaches are only safe if the approxi
Externí odkaz:
http://arxiv.org/abs/2004.11929
We study the invariance characteristics of pre-trained predictive models by empirically learning transformations on the input that leave the prediction function approximately unchanged. To learn invariant transformations, we minimize the Wasserstein
Externí odkaz:
http://arxiv.org/abs/1911.03295
Publikováno v:
In Smart Health December 2023 30
Autor:
Ravanbakhsh, Siamak, Oliva, Junier, Fromenteau, Sebastien, Price, Layne C., Ho, Shirley, Schneider, Jeff, Poczos, Barnabas
A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe. Galaxy su
Externí odkaz:
http://arxiv.org/abs/1711.02033
Autor:
Hotinli, Selim C., Frazer, Jonathan, Jaffe, Andrew H., Meyers, Joel, Price, Layne C., Tarrant, Ewan R. M.
Publikováno v:
Phys. Rev. D 97, 023511 (2018)
We study the sensitivity of cosmological observables to the reheating phase following inflation driven by many scalar fields. We describe a method which allows semi-analytic treatment of the impact of perturbative reheating on cosmological perturbati
Externí odkaz:
http://arxiv.org/abs/1710.08913
Autor:
Stott, Matthew J., Marsh, David J. E., Pongkitivanichkul, Chakrit, Price, Layne C., Acharya, Bobby S.
Publikováno v:
Phys. Rev. D 96, 083510 (2017)
Axions arise in many theoretical extensions of the Standard Model of particle physics, in particular the "string axiverse". If the axion masses, $m_a$, and (effective) decay constants, $f_a$, lie in specific ranges, then axions contribute to the cosm
Externí odkaz:
http://arxiv.org/abs/1706.03236