不如屎壳郎
发表于 2025-3-25 06:44:22
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空洞
发表于 2025-3-25 10:32:04
Obstacles to Depth Compression of Neural Networks any algorithm achieving depth compression of neural networks. In particular, we show that depth compression is as hard as learning the input distribution, ruling out guarantees for most existing approaches. Furthermore, even when the input distribution is of a known, simple form, we show that there are no . algorithms for depth compression.
勉强
发表于 2025-3-25 15:32:09
Prediction Stability as a Criterion in Active Learningect of the former uncertainty-based methods. Experiments are made on CIFAR-10 and CIFAR-100, and the results indicates that prediction stability was effective and works well on fewer-labeled datasets. Prediction stability reaches the accuracy of traditional acquisition functions like entropy on CIFAR-10, and notably outperformed them on CIFAR-100.
effrontery
发表于 2025-3-25 18:26:38
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言外之意
发表于 2025-3-25 20:57:06
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162650.jpg
敌手
发表于 2025-3-26 01:23:41
https://doi.org/10.1007/978-3-030-61616-8artificial intelligence; classification; computational linguistics; computer networks; computer vision; i
Geyser
发表于 2025-3-26 07:21:30
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金哥占卜者
发表于 2025-3-26 11:01:47
Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networksions. Log-Nets are capable of surpassing the performance of traditional convolutional neural networks (CNNs) while using fewer parameters. Performance is evaluated on the Cifar-10 and ImageNet benchmarks.
人充满活力
发表于 2025-3-26 12:52:15
Artificial Neural Networks and Machine Learning – ICANN 2020978-3-030-61616-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
委派
发表于 2025-3-26 17:47:01
,Einführung von Fertigungsinseln,ions. Log-Nets are capable of surpassing the performance of traditional convolutional neural networks (CNNs) while using fewer parameters. Performance is evaluated on the Cifar-10 and ImageNet benchmarks.