So from this paper. “Multi-Scale Context Aggregation by Dilated Convolutions”, I was introduced to Dilated Convolution Operation. And to be honest it is just convolution operation with modified kernel, to be exact, wider kernel.
However, to understand something fully, I need to implement it, hence the existence of this post.
Source: towardsdatascience.com
SNNs are really cool: their goal is to create neural networks in which, if the input of any layer is normally distributed, the output will automatically also be normally distributed. This is amazing because normalizing the output of layers is known to be a very efficient way to improve the performance of neural networks, but the current ways to do it (eg BatchNorm) basically involve weird hacks, while in SNN the normalization is an intrinsic part of the mathematics of the neural net.
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Since the ’50s, scientists have chased the promise of clean energy from sun-like reactions between deuterium and tritium, the plentiful isotopes of hydrogen. This carbon-free energy, achieved at temperatures of 360 million degrees Fahrenheit, would offer a great way to heat water and, in turn, spin turbines to create countless kilowatts of electricity.
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