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How to derive the formula 13 in the Xavier Initialization paper

How to derive the formula 13 in the Xavier Initialization paper Understanding the difficulty of training deep feedforward neural networks from the formula 6?
mon's user avatar
  • 791
0 votes
0 answers
149 views

calculating derivative of bias in backpropagation

Looking at the algorithm in wikipedia, we can implement backpropagation by calculating: $$\delta^{L}=\left(f^{L}\right)'\cdot\nabla_{a^{L}}C$$ (where I treat $\left(f^{L}\right)'$ as an $n\times n$ ...
Ariel Yael's user avatar
1 vote
0 answers
55 views

How to interpret integrated gradients in an NLP toxic text classification use-case?

I am trying to understand how integrated gradients work in the NLP case. Let $F: \mathbb{R}^{n} \rightarrow[0,1]$ a function representing a neural network, $x \in \mathbb{R}^{n}$ an input and $x' \in ...
Revolucion for Monica's user avatar
1 vote
0 answers
79 views

Which Neural Network or Gradient Boosting framework is the simplest for Custom Loss Functions?

I need to implement a custom loss function. The function is relatively simple: $$-\sum \limits_{i=1}^m [O_{1,i} \cdot y_i-1] \ \cdot \ \operatorname{ReLu}(O_{1,i} \cdot \hat{y_i} - 1)$$ With $O$ being ...
Borut Flis's user avatar
0 votes
1 answer
67 views

Vanishing gradient problem

In a neural network, does gradient vanish during a great number epochs as well, rather that only vanishing through different layers?
Domenico Bagnato's user avatar
2 votes
1 answer
531 views

Gradient passthough in PyTorch

I need to quantize the inputs, but the method (bucketize) I need to do so is indifferentiable. I can of course detach the tensor, but then I lose the flow of gradients to earlier weights. I guess ...
user3023715's user avatar
4 votes
3 answers
12k views

Forward pass vs backward pass vs backpropagation

As mentioned in the question, I have some issues understanding what are the differences between those terms. From what I have understood: Forward pass: compute the output of the network given the ...
Mattia Surricchio's user avatar
2 votes
1 answer
905 views

What does it mean for a method to be invariant to diagonal rescaling of the gradients?

In the paper which describes Adam: a method for stochastic optimization, the author states: The method is straightforward to implement, is computationally efficient, has little memory requirements, ...
dhulmul's user avatar
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