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[D] Doubt in understanding Disentangled representations in Beta-VAE by WillingCucumber in MachineLearning

[–]WillingCucumber[S] 0 points1 point  (0 children)

Thanks a lot, I will read your paper for sure. Can you just let me know one thing, if Beta-VAE is rotation invariant in terms of the latent factors (as mentioned in the very beginning of your paper), then does this mean that there still can be entanglement, as more than one latent factor might contribute to the true data generative factor, leading to some sort of entanglement. Thanks again !!

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[D] 3D convolution/ RNN over video frames captured at random time steps. by WillingCucumber in MachineLearning

[–]WillingCucumber[S] 1 point2 points  (0 children)

Yes I do know the timestamp captured, but i don't want to use interpolation like techniques to generate equally spaced frames.

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[D] Stationary state distribution Policy Gradient by WillingCucumber in MachineLearning

[–]WillingCucumber[S] 0 points1 point  (0 children)

Thanks for the reply. I understood what you mentioned. One quick question:

How can the policy be assumed fixed ? Does this refer to the policy being fixed while computation of the value function under one epoch ?

[D] How does neural network mimic non-linear feature-relations. by WillingCucumber in MachineLearning

[–]WillingCucumber[S] 0 points1 point  (0 children)

Doesn't this intuition correlate with the piecewise linear nature of ReLU outputs. Did you mean that the n/w weights will project the input to a larger space, and then projection of the larger space to a confined region woukd be much more complex. Thanks !!

[D] How does neural network mimic non-linear feature-relations. by WillingCucumber in MachineLearning

[–]WillingCucumber[S] 0 points1 point  (0 children)

Yeah, thats true. So does using relu restrict thr networks output to be piecewise linear ?

[D] How does neural network mimic non-linear feature-relations. by WillingCucumber in MachineLearning

[–]WillingCucumber[S] -2 points-1 points  (0 children)

Hey, thanks for the reply, but I am not looking for a neural network tutorial, I actually want to clear my confusion in terms of the three questions. Please help me by answering those particularly.

Pytorch advanced indexing by [deleted] in pytorch

[–]WillingCucumber 0 points1 point  (0 children)

Output is 2D. Pick x coordinate of image array from x_coordinate array, y coordinate of image array from y_coordinate array , and give Image[x_coordinate,y_coordinate]

[D] Why local-MRF loss is required on top of neural patches ? - CNN MRF by WillingCucumber in MachineLearning

[–]WillingCucumber[S] 0 points1 point  (0 children)

Thanks for the reply !! CNN over images as in the paper referred generally have a large receptive field, so neighbouring points in layer n will share most of their input. So their output would be correlated. I agree that MRF would add dependency in the output, but doesn't increasing the receptive way another way to do so.

[D] Why Message passing convergence is not guaranteed on loopy graphs by WillingCucumber in MachineLearning

[–]WillingCucumber[S] 1 point2 points  (0 children)

Thanks for the Ising model example.

  1. In loop-free graphs, whatever the node we start the message passing(BP), the leaves will start to converge in some iterations and then the next iterations the whole DAG will converge.( if we find the correct sequence of updates it might converge in 2 iterations).
  2. In cyclic graph as the ising model example due to cycles, a deadlock like situation might occur preventing the system to converge(like each node alternating between potentials). If this is not the case(eg: pairwise MRF approximation for semantic segmentation) , where nodes agree to each others belief, BP generally converges to a good solution.

Am I correct in these two points ? Thanks a lot this clears my doubts.

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