To enable more accurate estimation of connectivity, we propose a data-driven and theoretically grounded framework for optimally designing perturbation inputs, based on formulating the neural model as ...

Sasha S. Rao and Todd M. Hopfinger of Sterne, Kessler, Goldstein & Fox PLLC discuss challenges in meeting patent law's disclosure requirements for inventions involving artificial intelligence, ...

Abstract: Spiking neural networks (SNNs) are attractive algorithms that pose numerous potential advantages over traditional neural networks. One primary benefit of SNNs is that they may be run ...

Citation O. Taran, S. Bonev, and S. Voloshynovskiy, "Clonability of anti-counterfeiting printable graphical codes: a machine learning approach," in Proc. IEEE International Conference on Acoustics, ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

In this video, we will see What is Activation Function in Neural network, types of Activation function in Neural Network, why to use an Activation Function and which Activation function to use. The ...

Background In an ophthalmology emergency department, determining treatment urgency is crucial for patient safety and the efficient use of resources. The aim of this study was to use artificial ...

Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the ...

This paper is dedicated to the memory of my grandma. This is probably the only paper in my life that was written in a hospital the entire time. This is also at the same time the very first work in my ...