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The white noise that fools DNNs isn't really white noise. It has been altered in the same way as the synthetic misclassified pictures have been altered. You have to change many input pixels in exactly such a way, that these little changes aren't perceptible, but propagated through the network add up to a misclassification. This is not going to happen by ...


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As per this site Researchers recorded the complex patterns of electrical activity generated by someone’s brain, as the subject listened to someone talking. By feeding those brainwave patterns into a computer, they were able to translate them back into actual words — the same words that the volunteer had been hearing. The scientists behind the work ...


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There has been previous research with promising results cited at length in the following recent article, and although they have limited training data, here is some impressive research for an undergraduate thesis at the University of Arkansas which extends that research using an artificial neural network on enhancing a classifying algorithm's capacity to ...


3

What You need are other ways of knowledge representation, such as semantic networks or conceptual graphs. there you can define any possible relation between your entities. the knowledge of "x related to 4" exactly fits into "frames" and "semantic networks". Jaynes in his book,discusses thoroughly what "plausibility" means and why we need to take into ...


2

Theoretically, there shouldn't be a problem copying either of the artificial brains in any state. Difficulty in measuring a state doesn't seem to really be a problem until you get down to the quantum level, where the means of measurement affect the state. The configuration of the artificial brains, including pathway structures and states, should be ...


2

After inner product, add them all to make one feature map. Am I right? yes, you are right. Then, can I reduce the number of weights in the filters? Because in this case, using three different n×n filters and adding them is same with using one n×n filter that is the summation of three filters. If you have transformed the image into greyscale then you no ...


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You should look into unsupervised learning, which is machine learning without a training set. CNN's are cool but they need a training set.


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Is this a task suited for a neural network Yes. You have choices in fact: A fully-connected network would be simplest architecture, and would work if you gave it some time window of samples (e.g. every 0.5 seconds or every 50 samples) and supervised training data - sets of samples with sensor readings and the ground truth value of whether the motor was on ...


1

In general, the expression "temporal feature" might refer to any feature that is associated with or changes over time. However, in the context of signal processing, a temporal feature might refer to any feature of the data before being transformed to the Fourier, frequency or spectral domain, using the Fourier transform. In this context, the domain of the ...


1

I don't know if this is what you want, but Artificial Intelligence Markup Language or simply AIML is something that you should consider. The only problem I see with this language is that it is not popular thus there aren't many compilers for it. Here is an example of AIML. Code from tutorials point : <aiml version = "1.0.1" encoding = "UTF-8"?> &...


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