RALES
发表于 2025-3-26 23:47:40
Šimon Tóth,Dalibor Klusáčekion. The task includes clustering and hash learning as special cases. Deep neural networks are promising to be used because they can model the non-linearity of data and scale to large datasets. However, their model complexity is huge, and therefore, we need to carefully regularize the networks in or
Nonporous
发表于 2025-3-27 01:24:08
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Leaven
发表于 2025-3-27 09:16:24
Uwe Schwiegelshohn real-life data often consists of diverse data types. The unsupervised nature of clustering emphasizes the need to understand the criteria for defining and distinguishing clusters. Current explainable AI (XAI) methods for clustering focus on intrinsically explainable clustering techniques, surrogate
PAEAN
发表于 2025-3-27 12:21:05
tions of input images in many cases. Consequently, heatmaps have also been leveraged for achieving weakly supervised segmentation with image-level supervision. On the other hand, losses can be imposed on differentiable heatmaps, which has been shown to serve for (1) improving heatmaps to be more hum
Geyser
发表于 2025-3-27 15:32:55
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教唆
发表于 2025-3-27 21:31:32
Real-World Clustering for Task Graphs on Shared Memory Systems,increasingly important. Previous . scheduling algorithms assume negligible run-time overhead of spawning and joining tasks. We show that this overhead is significant for small- to medium-sized tasks which can often be found in automatically generated task graphs and in existing parallel applications
妨碍议事
发表于 2025-3-28 01:28:02
Experimental Analysis of the Tardiness of Parallel Tasks in Soft Real-Time Systems,programming technique to take advantage of the hardware advancement in processors manufacturing nowadays. In real-time systems, where tasks have to respect certain timing constraints during execution, a single task has a shorter response time when executed in parallel than the sequential execution.
Mucosa
发表于 2025-3-28 03:53:59
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Narcissist
发表于 2025-3-28 09:00:47
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BROW
发表于 2025-3-28 13:44:11
User-Aware Metrics for Measuring Quality of Parallel Job Schedules, the most notable problems is our inability to efficiently analyze the quality of schedules. While it is still possible to observe and measure certain aspects of generated schedules using various metrics, it is very challenging to choose a set of metrics that would be representative when measuring t