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See https://hal.archives-ouvertes.fr/hal-02968975/document
Implement: For multivariate Bernoulli, Sample a single normal sample, then for each dimension, use this sample and flip the corresponding…
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When I used the mamba network, I defined a loss to test backpropagation and found that the calculation was very slow. Setting the len length to 1024 requires a long waiting time. code show as below:
…
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Hi Ilya,
First of all thanks for sharing your code. It has been very useful to me lately. This is more of a question rather than an issue:
When you update the recurrent policy, how many steps ar…
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if the output changes and it's a smaller subset of possible output values, then we might need to alter our backpropagation.
Example
```
c = a + b
mod(c, 2)
```
this changes the universe o…
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Gradient computation is unsupported if a variable is multiplied with a Sparse Matrix, even if we are not interested at all at the gradient w.r.t. the Sparse Matrix, but only w.r.t. the dense variable.…
IngLP updated
6 years ago
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You currently have the following code in the `Backpropagation` method:
```c++
if(node->our_turn && score > 0) {
node->stats.wins++;
} else if(!node->our_turn && score < 0) {
…
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http://arxiv.org/abs/1606.03401
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do you think it is possible to backprop the neat result ? if so what do you think the best way(s) to do that ?
thanks!
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Let's say I have a network structure like this:
[0,1] -> PerceptronOne -> PerceptronTwo -> PerceptronThree -> [1,0]
( I'm using "->" shorthand for network.project(otherNetwork) )
if I call:
```
l…
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I couldn't get either methods for adjoint generation working over the `@parallel` stencil. For pure Zygote-based VJP calculations,
```julia
@parallel function diffusion3D_step!(T2, T, Ci, lam, dx…