We also analyse how different adversarial samples distort the attention of the neural network compared to original samples.
Thus, $\varphi$DNNs reveal that input recreation has strong benefits for artificial neural networks similar to biological ones, shedding light into the importance of purposely corrupting the input as well as pioneering an area of perception models based on GANs and autoencoders for robust recognition in artificial intelligence.
Here, we propose a continual generalization of the chunking problem (an unsupervised problem), encompassing fixed and probabilistic chunks, discovery of temporal and causal structures and their continual variations.
The main idea lies in the fact that some features are present on unknown classes and that unknown classes can be defined as a combination of previous learned features without representation bias (a bias towards representation that maps only current set of input-outputs and their boundary).
By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of concatenation layers in the search, we were able to evolve an architecture that is inherently accurate on adversarial samples.
A crucial step to understanding the rationale for this lack of robustness is to assess the potential of the neural networks' representation to encode the existing features.
There exists a vast number of adversarial attacks and defences for machine learning algorithms of various types which makes assessing the robustness of algorithms a daunting task.
Intrinsically, driving is a Markov Decision Process which suits well the reinforcement learning paradigm.
In this paper, both problems are solved together without approximations or simplifications.
Here, we go beyond attacks to investigate, for the first time, universal rules, i. e., rules that are sample agnostic and therefore could turn any text sample in an adversarial one.
The combination of Spectrum Diversity with a unified neuron representation enables the algorithm to either surpass or equal NeuroEvolution of Augmenting Topologies (NEAT) on all of the five classes of problems tested.
Here, a variation of the first algorithm is proposed which uses a parameterless self organizing map (SOM).
In fact, the proposed algorithm possesses a dynamical population structure that self-organizes itself to better project the input space into a map.
Experiments are conducted with the contingency training applied to neural networks over traditional datasets as well as datasets with additional irrelevant variables.
Moreover, NOTC is compared with NeuroEvolution of Augmenting Topologies (NEAT) in these problems, revealing a trade-off between the approaches.
The Internet of Things (IoT) is an extension of the traditional Internet, which allows a very large number of smart devices, such as home appliances, network cameras, sensors and controllers to connect to one another to share information and improve user experiences.
The results show that 67. 97% of the natural images in Kaggle CIFAR-10 test dataset and 16. 04% of the ImageNet (ILSVRC 2012) test images can be perturbed to at least one target class by modifying just one pixel with 74. 03% and 22. 91% confidence on average.