Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Regier, Jeffrey"'
For training an encoder network to perform amortized variational inference, the Kullback-Leibler (KL) divergence from the exact posterior to its approximation, known as the inclusive or forward KL, is an increasingly popular choice of variational obj
Externí odkaz:
http://arxiv.org/abs/2403.10610
Modern cell-perturbation experiments expose cells to panels of hundreds of stimuli, such as cytokines or CRISPR guides that perform gene knockouts. These experiments are designed to investigate whether a particular gene is upregulated or downregulate
Externí odkaz:
http://arxiv.org/abs/2307.11686
Telescopes capture images with a particular point spread function (PSF). Inferring what an image would have looked like with a much sharper PSF, a problem known as PSF deconvolution, is ill-posed because PSF convolution is not an invertible transform
Externí odkaz:
http://arxiv.org/abs/2307.11122
Amortized variational inference is an often employed framework in simulation-based inference that produces a posterior approximation that can be rapidly computed given any new observation. Unfortunately, there are few guarantees about the quality of
Externí odkaz:
http://arxiv.org/abs/2305.14275
Autor:
Patel, Yash, Regier, Jeffrey
Finding strong gravitational lenses in astronomical images allows us to assess cosmological theories and understand the large-scale structure of the universe. Previous works on lens detection do not quantify uncertainties in lens parameter estimates
Externí odkaz:
http://arxiv.org/abs/2211.10479
Coadded astronomical images are created by stacking multiple single-exposure images. Because coadded images are smaller in terms of data size than the single-exposure images they summarize, loading and processing them is less computationally expensiv
Externí odkaz:
http://arxiv.org/abs/2211.09300
In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes. However, the standard methods for causal survival analysis make simplistic assumptions about the data-generating process and cannot capture complex i
Externí odkaz:
http://arxiv.org/abs/2210.15417
Autor:
Loper, Jackson, Regier, Jeffrey
With the advent of high-throughput screenings, it has become increasingly common for studies to devote limited resources to estimating many parameters imprecisely rather than to estimating a few parameters well. In these studies, only two or three in
Externí odkaz:
http://arxiv.org/abs/2208.01745
Autor:
Hansen, Derek, Mendoza, Ismael, Liu, Runjing, Pang, Ziteng, Zhao, Zhe, Avestruz, Camille, Regier, Jeffrey
We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS). BLISS is based on deep generative models, which embed neural networks within a Bayesian model. Fo
Externí odkaz:
http://arxiv.org/abs/2207.05642
Controlled feature selection aims to discover the features a response depends on while limiting the false discovery rate (FDR) to a predefined level. Recently, multiple deep-learning-based methods have been proposed to perform controlled feature sele
Externí odkaz:
http://arxiv.org/abs/2106.01528