Ophthalmologist
发表于 2025-3-27 00:57:08
https://doi.org/10.1007/978-3-030-18274-8passing throughout the layers while maintaining model performance on previous tasks. Our analysis provides novel insights into information adaptation within the layers during incremental task learning. We provide empirical evidence and practically highlight the performance improvement across multiple tasks. Code is available at .
解脱
发表于 2025-3-27 01:17:51
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Adenoma
发表于 2025-3-27 06:23:27
https://doi.org/10.1007/978-1-349-63660-0rily-sized set of trainable prototypes. Our approach achieves competitive results over Deep Ensembles, the state of the art for uncertainty prediction, on image classification, segmentation and monocular depth estimation tasks. Our code is available at ..
GUISE
发表于 2025-3-27 13:01:28
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FLOAT
发表于 2025-3-27 15:00:58
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大笑
发表于 2025-3-27 19:02:43
,On the Angular Update and Hyperparameter Tuning of a Scale-Invariant Network,ochastic differential equation, we analyze the angular update and show how each hyperparameter affects it. With this relationship, we can derive a simple hyperparameter tuning method and apply it to the efficient hyperparameter search.
Pelago
发表于 2025-3-28 00:34:11
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调整校对
发表于 2025-3-28 05:49:39
https://doi.org/10.1007/978-1-349-02693-7dom numbers from different sources in neural networks and a generator-free framework is proposed for low-precision DNN training on a variety of deep learning tasks. Moreover, we evaluate the quality of the extracted random numbers and find that high-quality random numbers widely exist in DNNs, while their quality can even pass the NIST test suite.
作呕
发表于 2025-3-28 07:03:21
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Extricate
发表于 2025-3-28 11:06:07
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