One of the most intriguing phenomenons related to deep learning is so-called adversarial examples. These samples are visually equivalent normal inputs, undetectable for humans, yet they cause networks output wrong results. The phenomenon can be framed as a symmetry/asymmetry problem, whereby inputs neural network with similar/symmetric appearance regular images, produce an opposite/asymmetric o...

the concept of  -semihyperring is a generalization of semiring, a generalization of semihyperring and a generalization of      -semiring. since the theory of ideals plays an important role in the theory of  - semihyperring, in this paper, we will make an intensive study of the notions of noetherian, artinian, simple and   regular      -semihyperrings. the bulk of this paper...

This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use sin...

Recent research in inductive category learning has demonstrated that interleaved study of category exemplars results in better performance than does studying each category in separate blocks. However, the questions of how the category structure influences this advantage and how simultaneous presentation interacts with the advantage are open issues. In this article, we present three experiments....

The practice of learning from multiple instances seems to allow children to learn about relational structure. The experiments reported here focused on two issues regarding relational learning from multiple instances: (a) what kind of perceptual situations foster such learning and (b) how particular object properties, such as complexity and similarity, interact with relational learning. Two kind...

We continuously adapt our movements in daily life, forming new internal models whenever necessary and updating existing ones. Recent work has suggested that this flexibility is enabled via sensorimotor cues, serving to access the correct internal model whenever necessary and keeping new models apart from previous ones. While research to date has mainly focused on identifying the nature of such ...

Neural network design aims for high classification accuracy and low network architecture complexity. It is also known that simultaneous optimization of both model accuracy and complexity improves generalization while avoiding overfitting on data. We describe a neural network training procedure that uses multi-objective optimization to evolve networks which are optimal both with respect to class...