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pro vyhledávání: '"Out, P."'
Algebraic methods applied to the reconstruction of Sparse-view Computed Tomography (CT) can provide both a high image quality and a decrease in the dose received by patients, although with an increased reconstruction time since their computational co
Externí odkaz:
http://arxiv.org/abs/2412.07631
Autor:
Dalitz, Christoph, Lögler, Felix
The m-out-of-n bootstrap is a possible workaround to compute confidence intervals for bootstrap inconsistent estimators, because it works under weaker conditions than the n-out-of-n bootstrap. It has the disadvantage, however, that it requires knowle
Externí odkaz:
http://arxiv.org/abs/2412.05032
Modern machine learning models, that excel on computer vision tasks such as classification and object detection, are often overconfident in their predictions for Out-of-Distribution (OOD) examples, resulting in unpredictable behaviour for open-set en
Externí odkaz:
http://arxiv.org/abs/2412.01596
Skyrmions as well as skyrmion bags in magnetic thin films are promising candidates for future high-density memory devices. The observation of skyrmion bags in liquid crystals and their predicted existence in ferromagnetic films has sparked theoretica
Externí odkaz:
http://arxiv.org/abs/2411.18332
The deployment of autonomous vehicles controlled by machine learning techniques requires extensive testing in diverse real-world environments, robust handling of edge cases and out-of-distribution scenarios, and comprehensive safety validation to ens
Externí odkaz:
http://arxiv.org/abs/2411.16554
Autor:
Yang, Ying, Cheng, De, Fang, Chaowei, Wang, Yubiao, Jiao, Changzhe, Cheng, Lechao, Wang, Nannan
Publikováno v:
Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024)
Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for developing a safe real-world machine learning system. Current reconstruct
Externí odkaz:
http://arxiv.org/abs/2411.10701
Autor:
Andrianomena, Sambatra, Hassan, Sultan
We present an approach that can be utilized in order to account for the covariate shift between two datasets of the same observable with different distributions, so as to improve the generalizability of a neural network model trained on in-distributi
Externí odkaz:
http://arxiv.org/abs/2411.10515
Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we aim to address in this work. First, acquiring complete pixel-level segmentation labels for medical
Externí odkaz:
http://arxiv.org/abs/2411.09553
Consider the following stochastic matching problem. Given a graph $G=(V, E)$, an unknown subgraph $G_p = (V, E_p)$ is realized where $E_p$ includes every edge of $E$ independently with some probability $p \in (0, 1]$. The goal is to query a sparse su
Externí odkaz:
http://arxiv.org/abs/2411.08805
Autor:
Xu, Junhuai, Qin, Zhi, Zou, Renjie, Si, Dawei, Xiao, Sheng, Tian, Baiting, Wang, Yijie, Xiao, Zhigang
By combining the femtoscopic interferometry and the optical deblurring algorithm, we implement a novel method to image the source formed in HICs, while the interaction strength between the particle pair can be simultaneously determined. The spatial d
Externí odkaz:
http://arxiv.org/abs/2411.08718