Note: Can you answer the part b correctly? I$\text{'}$m posting for the third time now.. past $2$ people answered it wrong so please pass it to someone else if you are not sure.. Thank you in advance. If the answer is correct, I will give an upvote.

Q$1$ part a:

Let's now train multiple layers of temporal convolutions $(10$ in total$)$ with dilation on a large dataset where each

data point has $T=1000$ timesteps. All temporal convolutions are of size $2$ and dilated with $2^k$ for layers

$k=0,$dots$9.$ How many matrix multiplications does the gradient go through from $l_T$ to $x_0?$ Consider the most

efficlent implementation.

$logT$

$T$

$T^2$

$\sqrt[\phantom{2}]{T}$

Q$1$ part b:

Q$3$

Let's now dilate silghtly differently. All temporal convolutions are of size $2$ and dllated with $k+1$ for layers

$k=0$dots We increase the number of layers until the receptive field is larger than $1000.$ How many matrix

multiplications does the gradient go through from $l_T$ to $x_0?$ Consider the most efficient implementation.

$logT$

$T$

$T^2$

$\sqrt[\phantom{2}]{T}$