Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2022; 31st International C Elias Pimenidis,Plamen Angelov,Mehmet Aydin Conference p

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Magnificent

,Feature Selection for Trustworthy Regression Using Higher Moments,egression can be extended to take into account the complete distribution by making use of higher moments. We prove that the resulting method can be applied to preserve various certainty measures for regression tasks, including variance and confidence intervals, and we demonstrate this in example app

NEEDY

Commonwealth

,Multi-scale Feature Extraction and Fusion for Online Knowledge Distillation,e and fuse the former processed feature maps via feature fusion to assist the training of student models. Extensive experiments on CIFAR-10, CIFAR-100, and CINIC-10 show that MFEF transfers more beneficial representational knowledge for distillation and outperforms alternative methods among various

模范

,Ranking Feature-Block Importance in Artificial Multiblock Neural Networks,gs, knock-in and knock-out strategies evaluate the block as a whole via a mutual information criterion. Our experiments consist of a simulation study validating all three approaches, followed by a case study on two distinct real-world datasets to compare the strategies. We conclude that each strateg

锯齿状

,Stimulates Potential for Knowledge Distillation,eatures are transferred to the student to guide the student network learning. Extensive experimental results demonstrate that our SPKD has achieved significant classification results on the benchmark datasets CIFAR-10 and CIFAR-100.

Debate

Artificial Neural Networks and Machine Learning – ICANN 202231st International C

悬崖

我不明白

Schleifbarkeit unterschiedlicher Werkstoffe,tion process to extract the dark knowledge from the old task model to alleviate the catastrophic forgetting. We compare KRCL with the Finetune, LWF, IRCL and KRCL_real baseline methods on four benchmark datasets. The result shows that the KRCL model achieves state-of-the-art performance in standard

澄清