Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2020; 29th International C Igor Farka?,Paolo Masulli,Stefan Wermter Conference proc

不如屎殼郎

空洞

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.

勉強(qiáng)

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

言外之意

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162650.jpg

敵手

https://doi.org/10.1007/978-3-030-61616-8artificial intelligence; classification; computational linguistics; computer networks; computer vision; i

Geyser

金哥占卜者

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.

人充滿活力

Artificial Neural Networks and Machine Learning – ICANN 2020978-3-030-61616-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

委派

,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.