薄膜 发表于 2025-3-23 13:31:21
https://doi.org/10.1007/b138937tremely high accuracy levels in many fields. However, they still encounter many challenges. In particular, the models are not explainable or easy to trust, especially in life and death scenarios. They may reach correct predictions through inappropriate reasoning and have biases or other limitations.prodrome 发表于 2025-3-23 16:14:56
http://reply.papertrans.cn/17/1624/162310/162310_12.pngCosmopolitan 发表于 2025-3-23 21:36:04
http://reply.papertrans.cn/17/1624/162310/162310_13.pngepicondylitis 发表于 2025-3-23 22:46:33
https://doi.org/10.1007/b138937 of an image using classical information requires a huge amount of computational resources. Hence, exploring techniques for representing images in a different information paradigm is important. This paper describes the variety of options for representing images in quantum information. Image processiLAIR 发表于 2025-3-24 05:26:36
http://reply.papertrans.cn/17/1624/162310/162310_15.pngAsperity 发表于 2025-3-24 06:34:41
http://reply.papertrans.cn/17/1624/162310/162310_16.pngaltruism 发表于 2025-3-24 11:54:28
https://doi.org/10.1007/b138937ected only during normal behaviors. We also consider the problem of detecting which group of sensors is most affected by the anomalous situation solving an open-set classification task. The proposed methods are domain independent and are based on a temporal analysis of data collected by the system.cogent 发表于 2025-3-24 15:02:57
http://reply.papertrans.cn/17/1624/162310/162310_18.pngBLINK 发表于 2025-3-24 21:04:08
Karlheinz Lohs,Peter Elstner,Ursula Stephanction, classification, systems’ misbehaviour, etc. In this paper, we focus on generalizing the K-Means clustering approach when involving linear constraints on the clusters’ size. Indeed, to avoid local optimum clustering solutions which consists in empty clusters or clusters with few points, we proslipped-disk 发表于 2025-3-25 00:57:09
Teubner Reihe WirtschaftsinformatikThe method is especially useful for localizing objects in images. Here, we extend the method to the task of joint localization of several objects in a 2D-image by means of combining several centroids. The novel approach, i.e. joint optimization of several centroids and a subsequent optimization of t