Variational autoencoders (VAES) are generative autoencoders - they generate new data from the input. What is the coding fed into the decoder in these models? Instruction: Choose the option that best answers the question. The latent representation produced by the encoder represented in lower dimensionality The decoder directly takes in the input image specified using statistical parameters - VAES do not use encoder models The mean and standard deviation of the input distribution of the data used to train the model A random sample from a Gaussian distribution with mean and standard deviation produced by the encoder

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The input is a ×ばつ20 matrix and the filter (aka kernel) is a ×ばつ5 matrix. We set stride = 1 and no zero-padding. What is the shape of the output matrix

Describe the procedure for Monte Carlo localization using a particle filter.

Matlab: Create a linearly polarized light with its polarization along the X axis. (ii) Specify the polarization angle of the linear polarizer with respect to the X axis. (iii) Calculate the fields EX(t) and EY(t) after the polarizer. (iv) Similar with (a) (iii), plot the output vectorial fields ( EX(t), EY(t) after the polarizer) and the input vectorial fields (EX(t), EY(t) before the polarizer) in ONE time-lapse plot.

Use the MATLAB function marcumq to write a program to plot the CDF of a Rician random variable. Your program should take as inputs the two parameters a  and σ of the Rician random variable and output a plot of the CDF. Be sure to correctly label the axes on your plot.