Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Gao, Angela F."'
Regression on function spaces is typically limited to models with Gaussian process priors. We introduce the notion of universal functional regression, in which we aim to learn a prior distribution over non-Gaussian function spaces that remains mathem
Externí odkaz:
http://arxiv.org/abs/2404.02986
We consider solving ill-posed imaging inverse problems without access to an image prior or ground-truth examples. An overarching challenge in these inverse problems is that an infinite number of images, including many that are implausible, are consis
Externí odkaz:
http://arxiv.org/abs/2304.05589
We consider solving ill-posed imaging inverse problems without access to an explicit image prior or ground-truth examples. An overarching challenge in inverse problems is that there are many undesired images that fit to the observed measurements, thu
Externí odkaz:
http://arxiv.org/abs/2303.12217
Seismic waveform modeling is a powerful tool for determining earth structure models and unraveling earthquake rupture processes, but it is usually computationally expensive. We introduce a scheme to vastly accelerate these calculations with a recentl
Externí odkaz:
http://arxiv.org/abs/2209.11955
Autor:
Yang, Yan, Gao, Angela F., Castellanos, Jorge C., Ross, Zachary E., Azizzadenesheli, Kamyar, Clayton, Robert W.
Seismic wave propagation forms the basis for most aspects of seismological research, yet solving the wave equation is a major computational burden that inhibits the progress of research. This is exacerbated by the fact that new simulations must be pe
Externí odkaz:
http://arxiv.org/abs/2108.05421
Autor:
Gao, Angela F., Rasmussen, Brandon, Kulits, Peter, Scheller, Eva L., Greenberger, Rebecca, Ehlmann, Bethany L.
The application of infrared hyperspectral imagery to geological problems is becoming more popular as data become more accessible and cost-effective. Clustering and classifying spectrally similar materials is often a first step in applications ranging
Externí odkaz:
http://arxiv.org/abs/2106.13315
We consider solving ill-posed imaging inverse problems without access to an image prior or ground-truth examples. An overarching challenge in these inverse problems is that an infinite number of images, including many that are implausible, are consis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::431c281b4d609e0ff44b5b06f49472c7
http://arxiv.org/abs/2304.05589
http://arxiv.org/abs/2304.05589
Akademický článek
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Typically, inversion algorithms assume that a forward model, which relates a source to its resulting measurements, is known and fixed. Using collected indirect measurements and the forward model, the goal becomes to recover the source. When the forwa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________38::409dac9e887e73fa5b4fb0175d7915e5
https://resolver.caltech.edu/CaltechAUTHORS:20221026-200931912
https://resolver.caltech.edu/CaltechAUTHORS:20221026-200931912