Zobrazeno 1 - 10
of 355
pro vyhledávání: '"Willett, Rebecca"'
We study iterative signal reconstruction in computed tomography (CT), wherein measurements are produced by a linear transformation of the unknown signal followed by an exponential nonlinear map. Approaches based on pre-processing the data with a log
Externí odkaz:
http://arxiv.org/abs/2407.12984
We propose a new framework for algorithmic stability in the context of multiclass classification. In practice, classification algorithms often operate by first assigning a continuous score (for instance, an estimated probability) to each possible lab
Externí odkaz:
http://arxiv.org/abs/2405.14064
Autor:
Melia, Owen, Tsang, Olivia, Charisopoulos, Vasileios, Khoo, Yuehaw, Hoskins, Jeremy, Willett, Rebecca
Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestruc
Externí odkaz:
http://arxiv.org/abs/2405.13214
Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts. This paper investigates online weather prediction using machine learning surrogates supplemented w
Externí odkaz:
http://arxiv.org/abs/2405.13180
Model averaging techniques based on resampling methods (such as bootstrapping or subsampling) have been utilized across many areas of statistics, often with the explicit goal of promoting stability in the resulting output. We provide a general, finit
Externí odkaz:
http://arxiv.org/abs/2405.09511
We demonstrate that adding a weighting factor to decay the strength of identity shortcuts within residual networks substantially improves semantic feature learning in the state-of-the-art self-supervised masked autoencoding (MAE) paradigm. Our modifi
Externí odkaz:
http://arxiv.org/abs/2404.10947
We study depth separation in infinite-width neural networks, where complexity is controlled by the overall squared $\ell_2$-norm of the weights (sum of squares of all weights in the network). Whereas previous depth separation results focused on separ
Externí odkaz:
http://arxiv.org/abs/2402.08808
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
Rotational invariance is a popular inductive bias used by many fields in machine learning, such as computer vision and machine learning for quantum chemistry. Rotation-invariant machine learning methods set the state of the art for many tasks, includ
Externí odkaz:
http://arxiv.org/abs/2308.06271
Publikováno v:
PMLR 238:1540-1548, 2024
Conformalized Quantile Regression (CQR) is a recently proposed method for constructing prediction intervals for a response $Y$ given covariates $X$, without making distributional assumptions. However, existing constructions of CQR can be ineffective
Externí odkaz:
http://arxiv.org/abs/2306.08693
This paper studies distribution-free inference in settings where the data set has a hierarchical structure -- for example, groups of observations, or repeated measurements. In such settings, standard notions of exchangeability may not hold. To addres
Externí odkaz:
http://arxiv.org/abs/2306.06342